{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wSrFyZjRHQyt",
        "outputId": "30b5a1d4-6dcf-4909-9457-00c9ea383aa6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_path = '/content/drive/MyDrive/mit-bih-arrhythmia-database-1.0.0/'"
      ],
      "metadata": {
        "id": "5MsSCdyqJxYI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import wfdb\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "\n",
        "from scipy.signal import savgol_filter, find_peaks\n",
        "from scipy.interpolate import interp1d\n",
        "from scipy.interpolate import interp1d\n",
        "from scipy.signal import butter, filtfilt\n",
        "\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from collections import Counter\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n",
        "from sklearn.model_selection import StratifiedKFold"
      ],
      "metadata": {
        "id": "55r5Xzo3HadC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install wfdb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CBUP9Vh5W22_",
        "outputId": "dc5ff2df-3ce7-4e2b-b4e3-55daa466a039"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting wfdb\n",
            "  Downloading wfdb-4.1.2-py3-none-any.whl (159 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/160.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.7/160.0 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m160.0/160.0 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: SoundFile>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from wfdb) (0.12.1)\n",
            "Requirement already satisfied: matplotlib>=3.2.2 in /usr/local/lib/python3.10/dist-packages (from wfdb) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.10.1 in /usr/local/lib/python3.10/dist-packages (from wfdb) (1.23.5)\n",
            "Requirement already satisfied: pandas>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from wfdb) (1.5.3)\n",
            "Requirement already satisfied: requests>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from wfdb) (2.31.0)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from wfdb) (1.11.4)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb) (4.45.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.2.2->wfdb) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.3.0->wfdb) (2023.3.post1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.8.1->wfdb) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.8.1->wfdb) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.8.1->wfdb) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.8.1->wfdb) (2023.11.17)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.10/dist-packages (from SoundFile>=0.10.0->wfdb) (1.16.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0->SoundFile>=0.10.0->wfdb) (2.21)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.2.2->wfdb) (1.16.0)\n",
            "Installing collected packages: wfdb\n",
            "Successfully installed wfdb-4.1.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import wfdb"
      ],
      "metadata": {
        "id": "ZavFB4772Sj-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total_rpeaks"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Nvq2iDXCMUmd",
        "outputId": "ebeef5ab-4a61-47dd-b2a2-5054aade597d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-d360117bc8f4>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtotal_rpeaks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'total_rpeaks' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# File extensions for data and annotations\n",
        "dat_file_extension = 'dat'\n",
        "atr_file_extension = 'atr'\n",
        "\n",
        "heartbeats = []\n",
        "labels = []\n",
        "\n",
        "total_rpeaks = 0\n",
        "total_heatbeats = 0\n",
        "total_labels = 0\n",
        "\n",
        "def clean_data(data):\n",
        "    # Remove outliers\n",
        "    data = np.clip(data, -10, 10)\n",
        "\n",
        "    # Correct baseline wander\n",
        "    data = data - np.mean(data)\n",
        "\n",
        "    return data\n",
        "\n",
        "def normalize_data(data):\n",
        "\n",
        "    data = (data - np.min(data)) / (np.max(data) - np.min(data))\n",
        "\n",
        "    return data"
      ],
      "metadata": {
        "id": "w8DCZo7TNAGB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for record_number in [100, 101, 102, 103, 104, 105, 106, 107, 108, 109,\n",
        "                      111, 112, 113, 114, 115, 116, 117, 118, 119,\n",
        "                      121, 122, 123, 124,\n",
        "                      200, 201, 202, 203, 205, 207, 208, 209, 210,\n",
        "                      212, 213, 214, 215, 217, 219, 220, 221, 222, 223, 228, 230, 231, 232, 233, 234]:\n",
        "\n",
        "    record_name = str(record_number)\n",
        "\n",
        "    try:\n",
        "        # Load ECG data from the .dat file\n",
        "        record = wfdb.rdrecord(os.path.join(data_path, record_name))\n",
        "        ecg_data = record.p_signal[:, 0]\n",
        "\n",
        "        # Load annotations from the .atr file\n",
        "        annotation = wfdb.rdann(os.path.join(data_path, record_name), atr_file_extension)\n",
        "\n",
        "        # Clean ECG data\n",
        "        # Replace this with your clean_data function\n",
        "        ecg_data = clean_data(ecg_data)\n",
        "\n",
        "        # Normalize the ECG signal to the range [0, 1]\n",
        "        # Replace this with your normalize_data function\n",
        "        ecg_data = normalize_data(ecg_data)\n",
        "\n",
        "        # Find R-peaks in the ECG signal\n",
        "        r_peaks, _ = find_peaks(ecg_data, height=0.2, distance=200)\n",
        "\n",
        "        for r_peak in r_peaks:\n",
        "            total_rpeaks = total_rpeaks + 1\n",
        "            # Determine the annotation label for the current R-peak\n",
        "            annotation_indices = np.where(annotation.sample == r_peak)[0]\n",
        "\n",
        "            if annotation_indices.size > 0:\n",
        "                # Use the first matching annotation index\n",
        "                annotation_index = annotation_indices[0]\n",
        "                annotation_label = annotation.symbol[annotation_index]\n",
        "\n",
        "            start = r_peak - 100  # Adjust the window size as needed\n",
        "            end = r_peak + 100    # Adjust the window size as needed\n",
        "            if start < 0 or end >= len(ecg_data):\n",
        "                continue\n",
        "\n",
        "            heartbeat_segment = ecg_data[start:end]\n",
        "            heartbeats.append(heartbeat_segment)\n",
        "            labels.append(annotation_label)\n",
        "\n",
        "            total_heatbeats = total_heatbeats + 1\n",
        "            total_labels = total_labels + 1\n",
        "\n",
        "        aami_categories = {'N': [], 'S': [], 'V': [], 'F': [], 'Q': []}\n",
        "\n",
        "        for i in range(len(heartbeats)):\n",
        "            annotation_l = labels[i]\n",
        "            heartbeat = heartbeats[i]\n",
        "\n",
        "            if annotation_l in ['N', 'L', 'R', 'e', 'j']:\n",
        "                aami_categories['N'].append(heartbeat)\n",
        "\n",
        "            elif annotation_l in ['V', 'E']:\n",
        "                aami_categories['V'].append(heartbeat)\n",
        "\n",
        "            elif annotation_l in ['A', 'a', 'J', 'S']:\n",
        "                aami_categories['S'].append(heartbeat)\n",
        "\n",
        "            elif annotation_l == 'F':\n",
        "                aami_categories['F'].append(heartbeat)\n",
        "\n",
        "            elif annotation_l in ['P', '/', 'f', 'Q', 'U']:\n",
        "                aami_categories['Q'].append(heartbeat)\n",
        "\n",
        "            else:\n",
        "                continue\n",
        "\n",
        "    except FileNotFoundError:\n",
        "        # Handle the case where the file does not exist\n",
        "        print(f\"File '{record_name}' not found. Skipping...\")"
      ],
      "metadata": {
        "id": "f2k75gW5Mhx6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os"
      ],
      "metadata": {
        "id": "-1c5oLtuL0Ns"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loop through the AAMI categories and print the counts\n",
        "total = 0\n",
        "for category, heartbeats in aami_categories.items():\n",
        "    count = len(heartbeats)\n",
        "    print(f'Category {category}: {count} heartbeats')\n",
        "    total = total + count\n",
        "\n",
        "print(f'Total segments from all Category {total}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y_HwnLyCNz7f",
        "outputId": "05aeca47-3ee5-49d2-900e-567df61cda5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Category N: 95696 heartbeats\n",
            "Category S: 2486 heartbeats\n",
            "Category V: 8347 heartbeats\n",
            "Category F: 512 heartbeats\n",
            "Category Q: 4294 heartbeats\n",
            "Total segments from all Category 111335\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training (70%), validation (15%), and test (15%) sets\n",
        "X = []  # Features\n",
        "y = []  # Target variable\n",
        "\n",
        "for category, heartbeats in aami_categories.items():\n",
        "    if heartbeats:\n",
        "        # Append the features and labels\n",
        "        X.extend(heartbeats)\n",
        "        y.extend([category] * len(heartbeats))\n",
        "\n",
        "# Convert to numpy arrays\n",
        "X = np.array(X)\n",
        "y = np.array(y)"
      ],
      "metadata": {
        "id": "p-SvHourOHkj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QVjkVX1lWKh-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(data=X, columns=[f'value{i}' for i in range(1, X.shape[1] + 1)])\n",
        "df['label'] = y\n",
        "\n",
        "# Save the entire dataset to a CSV file\n",
        "df.to_csv('final.csv', index=False)"
      ],
      "metadata": {
        "id": "zfEnjzCJT3pr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Length of X (heartbeats): {len(X)}')\n",
        "print(f'Length of y (labels): {len(y)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_8j71MXO59L",
        "outputId": "17bbe69f-4645-4ab3-988d-1320629ac1b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of X (heartbeats): 111335\n",
            "Length of y (labels): 111335\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a dictionary to count the number of segments for each annotation type\n",
        "num_segments_per_category = {}\n",
        "\n",
        "# Create a single figure for all annotation types\n",
        "plt.figure(figsize=(15, 8))\n",
        "\n",
        "# Visualize each type of annotation\n",
        "for i, (aami_label, segments) in enumerate(aami_categories.items()):\n",
        "    num_segments = len(segments)\n",
        "    num_segments_per_category[aami_label] = num_segments\n",
        "\n",
        "    for j, segment in enumerate(segments[:1]):\n",
        "        plt.subplot(5, len(aami_categories), i * 5 + j + 1)\n",
        "        plt.plot(segment)\n",
        "        plt.title(f'{aami_label} - Segment {j + 1}')\n",
        "        plt.xlabel('Sample')\n",
        "        plt.ylabel('Amplitude')\n",
        "\n",
        "# Adjust layout and show the figure\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Print the number of segments for each annotation type\n",
        "for aami_label, num_segments in num_segments_per_category.items():\n",
        "    if(aami_label == 'S'):\n",
        "        num_seg_s = num_segments\n",
        "    elif(aami_label == 'F'):\n",
        "        num_seg_f = num_segments\n",
        "    elif(aami_label == 'N'):\n",
        "        num_seg_n = num_segments\n",
        "    elif(aami_label == 'V'):\n",
        "        num_seg_v = num_segments\n",
        "    elif(aami_label == 'Q'):\n",
        "        num_seg_q = num_segments\n",
        "\n",
        "    print(f'AAMI Class Type: {aami_label}, Number of Segments: {num_segments}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "Njr5kLMFYc9r",
        "outputId": "027049ec-9bec-4e18-b27e-ded3c2bf05fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 5 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the desired number of samples for S and F\n",
        "desired_samples_S = 3*num_seg_s\n",
        "desired_samples_F = 3*num_seg_f\n",
        "\n",
        "# Create a dictionary with the desired number of samples for each class\n",
        "sampling_strategy = {'F': desired_samples_F, 'S': desired_samples_S}\n",
        "\n",
        "# Use RandomOverSampler to oversample the minority classes (F and S) in the entire dataset\n",
        "oversampler = RandomOverSampler(sampling_strategy=sampling_strategy, random_state=42)\n",
        "\n",
        "# Fit and transform the dataset\n",
        "X_resampled, y_resampled = oversampler.fit_resample(X, y)\n",
        "\n",
        "# Check the number of samples for S and F after resampling\n",
        "Total = num_seg_n + sum(y_resampled == 'S') + num_seg_v + num_seg_q + sum(y_resampled == 'F')\n",
        "\n",
        "print(\"Total Number of samples after oversampling minority class:\", Total)\n",
        "print()\n",
        "print(\"Number of samples for 'N' after oversampling minority class:\", num_seg_n)\n",
        "print(\"Number of samples for 'S' after oversampling minority class:\", sum(y_resampled == 'S'))\n",
        "print(\"Number of samples for 'V' after oversampling minority class:\", num_seg_v)\n",
        "print(\"Number of samples for 'F' after oversampling minority class:\", sum(y_resampled == 'F'))\n",
        "print(\"Number of samples for 'Q' after oversampling minority class:\", num_seg_q)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XbHJ-O7uUBT1",
        "outputId": "c670ff37-2d3a-41f3-8d8a-0f9562e49f7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Number of samples after oversampling minority class: 117331\n",
            "\n",
            "Number of samples for 'N' after oversampling minority class: 95696\n",
            "Number of samples for 'S' after oversampling minority class: 7458\n",
            "Number of samples for 'V' after oversampling minority class: 8347\n",
            "Number of samples for 'F' after oversampling minority class: 1536\n",
            "Number of samples for 'Q' after oversampling minority class: 4294\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install imbalanced-learn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VhgAD2prUSML",
        "outputId": "b54769a0-9557-4b88-9091-c608e653925b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.10/dist-packages (0.10.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.11.4)\n",
            "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.2.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (3.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(data=X_resampled, columns=[f'value{i}' for i in range(1, X_resampled.shape[1] + 1)])\n",
        "df['label'] = y_resampled\n",
        "\n",
        "# Save the entire dataset to a CSV file\n",
        "df.to_csv('final_randomoversampling.csv', index=False)"
      ],
      "metadata": {
        "id": "h2YPm9qIUai9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Total Number of samples after synthesizing minority class:\", Total)\n",
        "print()\n",
        "print(\"Number of samples for 'N' after synthesizing minority class:\", num_seg_n)\n",
        "print(\"Number of samples for 'S' after synthesizing minority class:\", sum(y_resampled == 'S'))\n",
        "print(\"Number of samples for 'V' after synthesizing minority class:\", num_seg_v)\n",
        "print(\"Number of samples for 'F' after synthesizing minority class:\", sum(y_resampled == 'F'))\n",
        "print(\"Number of samples for 'Q' after synthesizing minority class:\", num_seg_q)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k4xGTm29UzJx",
        "outputId": "c28c6782-e4d6-4660-a529-0676eb295757"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Number of samples after synthesizing minority class: 117331\n",
            "\n",
            "Number of samples for 'N' after synthesizing minority class: 95696\n",
            "Number of samples for 'S' after synthesizing minority class: 7458\n",
            "Number of samples for 'V' after synthesizing minority class: 8347\n",
            "Number of samples for 'F' after synthesizing minority class: 1536\n",
            "Number of samples for 'Q' after synthesizing minority class: 4294\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "oversampler"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "BEUpwo4lU4VJ",
        "outputId": "b51cb26e-ae08-43b1-8238-03144bad44c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomOverSampler(random_state=42, sampling_strategy={'F': 1536, 'S': 7458})"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomOverSampler(random_state=42, sampling_strategy={&#x27;F&#x27;: 1536, &#x27;S&#x27;: 7458})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomOverSampler</label><div class=\"sk-toggleable__content\"><pre>RandomOverSampler(random_state=42, sampling_strategy={&#x27;F&#x27;: 1536, &#x27;S&#x27;: 7458})</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Assuming X and y are your feature and target variables\n",
        "\n",
        "# Split the data into training, validation, and test sets\n",
        "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
        "\n",
        "# Specify the desired ratio for each class\n",
        "sampling_strategy = {'S': 3*num_seg_s, 'F': 3*num_seg_f}\n",
        "\n",
        "# Instantiate the SMOTE class with specified sampling_strategy\n",
        "smote = SMOTE(sampling_strategy=sampling_strategy, random_state=42)\n",
        "\n",
        "# Fit and transform the training data\n",
        "X_resampled_smote, y_resampled_smote = smote.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "vnLb1RvrU8SX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(data=X_resampled_smote, columns=[f'value{i}' for i in range(1, X_resampled_smote.shape[1] + 1)])\n",
        "df['label'] = y_resampled_smote\n",
        "\n",
        "# Save the entire dataset to a CSV file\n",
        "df.to_csv('final_smote.csv', index=False)"
      ],
      "metadata": {
        "id": "fB9aVjXmVQjX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "smote"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "I0hvt4wAVS7-",
        "outputId": "f8ffe5f0-6d41-4441-da47-12d8af4e6c3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SMOTE(random_state=42, sampling_strategy={'F': 1536, 'S': 7458})"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SMOTE(random_state=42, sampling_strategy={&#x27;F&#x27;: 1536, &#x27;S&#x27;: 7458})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SMOTE</label><div class=\"sk-toggleable__content\"><pre>SMOTE(random_state=42, sampling_strategy={&#x27;F&#x27;: 1536, &#x27;S&#x27;: 7458})</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_resampled_smote)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X5uenyMPVWm9",
        "outputId": "eea56c0f-ceae-40a6-f47f-aac95d1f00e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "84857"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **CNN - LSTM Model**"
      ],
      "metadata": {
        "id": "WjqA6CRm-fM_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers, models\n",
        "\n",
        "# Reshape the data\n",
        "X_train_reshaped = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
        "X_val_reshaped = X_val.reshape((X_val.shape[0], X_val.shape[1], 1))\n",
        "X_test_reshaped = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))\n",
        "\n",
        "# hybrid CNN-LSTM model\n",
        "model = models.Sequential()\n",
        "\n",
        "# CNN layers\n",
        "model.add(layers.Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(X_train.shape[1], 1)))\n",
        "model.add(layers.MaxPooling1D(pool_size=2))\n",
        "model.add(layers.Flatten())\n",
        "\n",
        "# Dense layer\n",
        "model.add(layers.Dense(200, activation='relu'))\n",
        "\n",
        "# Reshape data\n",
        "model.add(layers.Reshape((200, 1)))\n",
        "\n",
        "# LSTM layers\n",
        "model.add(layers.LSTM(64, return_sequences=True))\n",
        "model.add(layers.LSTM(64))\n",
        "\n",
        "# Dense layers\n",
        "model.add(layers.Dense(64, activation='relu'))\n",
        "model.add(layers.Dense(5, activation='softmax'))  # Adjust the number of output units based on your number of classes\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Display the model summary\n",
        "model.summary()\n",
        "\n",
        "history = model.fit(X_train_reshaped, y_train, epochs=10, validation_data=(X_val_reshaped, y_val))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "12HyTmnoWMXi",
        "outputId": "ddeb47ab-1113-4345-db36-25f302d6a5dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d_3 (Conv1D)           (None, 198, 32)           128       \n",
            "                                                                 \n",
            " max_pooling1d_3 (MaxPoolin  (None, 99, 32)            0         \n",
            " g1D)                                                            \n",
            "                                                                 \n",
            " flatten_3 (Flatten)         (None, 3168)              0         \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 200)               633800    \n",
            "                                                                 \n",
            " reshape_3 (Reshape)         (None, 200, 1)            0         \n",
            "                                                                 \n",
            " lstm_6 (LSTM)               (None, 200, 64)           16896     \n",
            "                                                                 \n",
            " lstm_7 (LSTM)               (None, 64)                33024     \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 64)                4160      \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 5)                 325       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 688333 (2.63 MB)\n",
            "Trainable params: 688333 (2.63 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "test_loss, test_accuracy = model.evaluate(X_test_reshaped, y_test)\n",
        "print(f'Test Accuracy: {test_accuracy * 100:.2f}%')\n",
        "\n",
        "# Plot the training history\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 831
        },
        "id": "EXpAWcNr3dgW",
        "outputId": "cad26252-2ced-4ec3-bc32-baa7a0d5c31c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "2436/2436 [==============================] - 53s 19ms/step - loss: 0.5014 - accuracy: 0.8677 - val_loss: 0.3987 - val_accuracy: 0.8837\n",
            "Epoch 2/10\n",
            "2436/2436 [==============================] - 45s 18ms/step - loss: 0.3543 - accuracy: 0.8942 - val_loss: 0.3320 - val_accuracy: 0.8965\n",
            "Epoch 3/10\n",
            "2436/2436 [==============================] - 42s 17ms/step - loss: 0.3030 - accuracy: 0.9029 - val_loss: 0.3119 - val_accuracy: 0.8953\n",
            "Epoch 4/10\n",
            "2436/2436 [==============================] - 41s 17ms/step - loss: 0.2798 - accuracy: 0.9103 - val_loss: 0.2720 - val_accuracy: 0.9079\n",
            "Epoch 5/10\n",
            "2436/2436 [==============================] - 42s 17ms/step - loss: 0.2642 - accuracy: 0.9145 - val_loss: 0.2543 - val_accuracy: 0.9166\n",
            "Epoch 6/10\n",
            "2436/2436 [==============================] - 42s 17ms/step - loss: 0.2543 - accuracy: 0.9162 - val_loss: 0.2600 - val_accuracy: 0.9108\n",
            "Epoch 7/10\n",
            "2436/2436 [==============================] - 44s 18ms/step - loss: 0.2458 - accuracy: 0.9173 - val_loss: 0.2367 - val_accuracy: 0.9184\n",
            "Epoch 8/10\n",
            "2436/2436 [==============================] - 42s 17ms/step - loss: 0.2387 - accuracy: 0.9194 - val_loss: 0.2336 - val_accuracy: 0.9201\n",
            "Epoch 9/10\n",
            "2436/2436 [==============================] - 44s 18ms/step - loss: 0.2332 - accuracy: 0.9219 - val_loss: 0.2250 - val_accuracy: 0.9217\n",
            "Epoch 10/10\n",
            "2436/2436 [==============================] - 40s 17ms/step - loss: 0.2292 - accuracy: 0.9212 - val_loss: 0.2224 - val_accuracy: 0.9234\n",
            "522/522 [==============================] - 5s 9ms/step - loss: 0.2395 - accuracy: 0.9198\n",
            "Test Accuracy: 91.98%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o23_0AZR3d0e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_reshaped = X_train_reshaped.astype('float32')\n",
        "X_val_reshaped = X_val_reshaped.astype('float32')\n",
        "X_test_reshaped = X_test_reshaped.astype('float32')\n"
      ],
      "metadata": {
        "id": "eFOk1rswZHl_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Combine all labels to fit the label encoder\n",
        "all_labels = np.concatenate((y_train, y_val, y_test))\n",
        "\n",
        "# Initialize the label encoder\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "# Fit and transform the labels\n",
        "all_labels_encoded = label_encoder.fit_transform(all_labels)\n",
        "\n",
        "# Split the encoded labels back to train, validation, and test sets\n",
        "y_train_encoded = all_labels_encoded[:len(y_train)]\n",
        "y_val_encoded = all_labels_encoded[len(y_train):(len(y_train) + len(y_val))]\n",
        "y_test_encoded = all_labels_encoded[(len(y_train) + len(y_val)):]\n",
        "\n",
        "# Convert the encoded labels to int32\n",
        "y_train = y_train_encoded.astype('int32')\n",
        "y_val = y_val_encoded.astype('int32')\n",
        "y_test = y_test_encoded.astype('int32')\n"
      ],
      "metadata": {
        "id": "YcrzhsqxZJHs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming you have trained and compiled your CNN model\n",
        "# model = ...\n",
        "\n",
        "# Get predictions for the validation set\n",
        "y_val_pred_prob = model.predict(X_val)\n",
        "\n",
        "# Convert predicted probabilities to class labels\n",
        "y_val_pred = np.argmax(y_val_pred_prob, axis=1)\n",
        "\n",
        "# Display result statistics for the validation set\n",
        "print(\"Validation Accuracy:\", accuracy_score(y_val, y_val_pred))\n",
        "print(\"\\nValidation Classification Report:\\n\", classification_report(y_val, y_val_pred))\n",
        "print(\"\\nValidation Confusion Matrix:\\n\", confusion_matrix(y_val, y_val_pred))\n",
        "\n",
        "# Get unique class labels from your data\n",
        "unique_labels = ['F', 'N', 'Q', 'S', 'V']\n",
        "\n",
        "# Plot confusion matrix for the validation set\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=confusion_matrix(y_val, y_val_pred),\n",
        "                               display_labels=unique_labels)\n",
        "disp.plot(cmap=plt.cm.Blues, values_format='d')\n",
        "plt.title('Confusion Matrix - Validation Set (CNN)')\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 976
        },
        "id": "7455F8fTZoha",
        "outputId": "acbdee2c-0288-4dff-b36e-0f08ab3648d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "522/522 [==============================] - 13s 7ms/step\n",
            "Validation Accuracy: 0.026047904191616768\n",
            "\n",
            "Validation Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.02      0.70      0.04        73\n",
            "           1       0.00      0.00      0.00     14345\n",
            "           2       0.00      0.00      0.00       645\n",
            "           3       0.03      0.97      0.05       396\n",
            "           4       0.00      0.00      0.00      1241\n",
            "\n",
            "    accuracy                           0.03     16700\n",
            "   macro avg       0.01      0.33      0.02     16700\n",
            "weighted avg       0.00      0.03      0.00     16700\n",
            "\n",
            "\n",
            "Validation Confusion Matrix:\n",
            " [[   51     0     1    21     0]\n",
            " [ 1840     0     9 12496     0]\n",
            " [  494     0     0   151     0]\n",
            " [   12     0     0   384     0]\n",
            " [  393     0     0   848     0]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict on the test set\n",
        "y_pred_probs = model.predict(X_test_reshaped)\n",
        "y_pred_classes = tf.argmax(y_pred_probs, axis=1)\n",
        "\n",
        "# Convert to numpy array\n",
        "y_pred_classes = y_pred_classes.numpy()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7PqLNDrKIycu",
        "outputId": "918fbc02-3bdd-4968-9073-a2f2f5f9b9e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "522/522 [==============================] - 5s 8ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_val_pred_prob = model.predict(X_val)\n",
        "y_val_pred = np.argmax(y_val_pred_prob, axis=1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bPTFsaYbJEzy",
        "outputId": "6e92c83e-50a9-47ab-9f32-25fb62f8d946"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "522/522 [==============================] - 4s 8ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Validation Accuracy:\", accuracy_score(y_val, y_val_pred))\n",
        "print(\"\\nValidation Classification Report:\\n\", classification_report(y_val, y_val_pred))\n",
        "print(\"\\nValidation Confusion Matrix:\\n\", confusion_matrix(y_val, y_val_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ZdgucMPJX1k",
        "outputId": "3ef29967-35b5-4b91-e7e9-f2464cb6b023"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Accuracy: 0.9234131736526946\n",
            "\n",
            "Validation Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.60      0.34      0.43        73\n",
            "           1       0.94      0.97      0.96     14345\n",
            "           2       0.95      0.89      0.92       645\n",
            "           3       0.63      0.40      0.49       396\n",
            "           4       0.70      0.56      0.62      1241\n",
            "\n",
            "    accuracy                           0.92     16700\n",
            "   macro avg       0.76      0.64      0.69     16700\n",
            "weighted avg       0.92      0.92      0.92     16700\n",
            "\n",
            "\n",
            "Validation Confusion Matrix:\n",
            " [[   25    41     0     0     7]\n",
            " [   15 13961    27    93   249]\n",
            " [    0    37   577     0    31]\n",
            " [    0   222     3   160    11]\n",
            " [    2   538     1     2   698]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm_percentage = cm / cm.sum(axis=1)[:, np.newaxis] * 100\n",
        "\n",
        "# Create a heatmap with percentages and a border around the entire plot\n",
        "plt.figure(figsize=(8, 6))\n",
        "ax = sns.heatmap(cm_percentage, annot=True, fmt='.1f', cmap='Blues', xticklabels=unique_labels, yticklabels=unique_labels,\n",
        "                 cbar_kws={'label': 'Percentage'})\n",
        "\n",
        "# Add a border around the entire plot\n",
        "for spine in ax.spines.values():\n",
        "    spine.set_edgecolor('black')\n",
        "    spine.set_linewidth(2)\n",
        "\n",
        "plt.title('CNN-LSTM confusion matrix')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "QdTrNRN5Jr3F",
        "outputId": "6385ad89-57bf-4f59-f1a2-3d66c6b52101"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAApQAAAIjCAYAAAC9AIvxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB0kElEQVR4nO3dd1gUVxcG8HdpS5PexIIoCChgjb2LsSXWz5LYu8aOFXtHjb13RGOJxhK7Els0dmOJih3EQq8inZ3vD3XjBlDWYVlg31+eeZ5w587dM7vsejj3zqxEEAQBRERERERfSUvdARARERFR4caEkoiIiIhEYUJJRERERKIwoSQiIiIiUZhQEhEREZEoTCiJiIiISBQmlEREREQkChNKIiIiIhKFCSURERERicKEkoiKlO3bt8PV1RW6urowMzPL8/FnzJgBiUSS5+MWdufOnYNEIsG5c+fUHQoRqQETStIoz549w6BBg1C2bFno6+vDxMQEdevWxfLly5GcnCzvV6ZMGUgkEgwfPjzLGB//4fztt9/kbVu3boVEIoG+vj5ev36d5ZhGjRrB3d09VzFKJBIMGzbss31kMhm2bduGmjVrwsLCAsWKFUP58uXRs2dPXLlyReEcvrRt3bpV/rgSiQT9+/fP9jEnT54s7xMVFZWrc8lvDx8+RO/evVGuXDls3LgRGzZsUHdIhdLOnTuxbNkydYdBRIWIjroDIMovR48eRadOnSCVStGzZ0+4u7sjLS0NFy9exLhx43D//v0sCcjGjRvh4+MDe3v7XD1Gamoq5s+fj5UrV6riFORGjBiB1atXo23btujWrRt0dHTw6NEjHD9+HGXLlkWtWrWwbNkyJCYmyo85duwYdu3ahaVLl8LKykreXqdOHfn/6+vrY9++fVizZg309PQUHnPXrl3Q19dHSkqKSs9NjHPnzkEmk2H58uVwcnJSyWNMmTIFEydOVMnYBcXOnTtx7949jBo1KtfHNGjQAMnJyVl+b4hIMzChJI0QFBSErl27wsHBAWfOnEHx4sXl+4YOHYqnT5/i6NGjCsdUrFgRjx49wvz587FixYpcPU7lypWVTkKVFR4ejjVr1mDAgAFZEuBly5YhMjISANCuXTuFfWFhYdi1axfatWuHMmXKZDt2ixYtcOjQIRw/fhxt27aVt1+6dAlBQUHo2LEj9u3bl6fnk5ciIiIAQCVT3R/p6OhAR4cfnR+lpKRAT08PWlpa0NfXV3c4RKQmnPImjbBw4UIkJiZi8+bNCsnkR05OThg5cqRCW5kyZdCzZ09s3LgRb968ydXjTJo0CZmZmZg/f36exJ2doKAgCIKAunXrZtknkUhgY2Pz1WOXKFECDRo0wM6dOxXad+zYAQ8Pj1xP2wPA69ev0a9fP9jb20MqlcLR0RFDhgxBWlqavM/z58/RqVMnWFhYwNDQELVq1cqS2H9cYrBnzx7MnTsXJUuWhL6+Ppo2bYqnT5/K+5UpUwbTp08HAFhbW0MikWDGjBkAoPD/nypTpgx69+4t/zk9PR0zZ86Es7Mz9PX1YWlpiXr16iEgIEDeJ7s1lBkZGZg9ezbKlSsHqVSKMmXKYNKkSUhNTc3yeN999x0uXryIGjVqQF9fH2XLlsW2bdu++HwGBwdDIpFg0aJFWL16NcqWLQtDQ0N8++23ePnyJQRBwOzZs1GyZEkYGBigbdu2iImJURjj999/R+vWreWvSbly5TB79mxkZmbK+zRq1AhHjx7Fixcv5EscPv4B8vG12L17N6ZMmYISJUrA0NAQCQkJWdZQBgYGwsDAAD179lSI4eLFi9DW1saECRO+eM5EVHjwz2zSCIcPH0bZsmUVpndzY/Lkydi2bVuuq5SOjo7yJHTixIkqqVI6ODgAAPbu3YtOnTrB0NAwT8f/8ccfMXLkSCQmJsLY2BgZGRnYu3cvvL29cz3d/ebNG9SoUQNxcXEYOHAgXF1d8fr1a/z2229ISkqCnp4ewsPDUadOHSQlJWHEiBGwtLSEv78/2rRpg99++w3t27dXGHP+/PnQ0tLC2LFjER8fj4ULF6Jbt264evUqgPfV2W3btuHAgQNYu3YtjI2N4enpqdS5z5gxA76+vujfvz9q1KiBhIQE3LhxA3///TeaNWuW43H9+/eHv78//ve//2HMmDG4evUqfH19ERgYiAMHDij0ffr0Kf73v/+hX79+6NWrF7Zs2YLevXujWrVqqFix4hdj3LFjB9LS0jB8+HDExMRg4cKF6Ny5M5o0aYJz585hwoQJePr0KVauXImxY8diy5Yt8mO3bt0KY2NjeHt7w9jYGGfOnMG0adOQkJCAn3/+GcD73/n4+Hi8evUKS5cuBQAYGxsrxDB79mzo6elh7NixSE1NzXaa283NDbNnz8a4cePwv//9D23atMG7d+/Qu3dvuLq6YtasWV88VyIqRASiIi4+Pl4AILRt2zbXxzg4OAitW7cWBEEQ+vTpI+jr6wtv3rwRBEEQzp49KwAQ9u7dK+/v5+cnABCuX78uPHv2TNDR0RFGjBgh39+wYUOhYsWKuXpsAMLQoUM/26dnz54CAMHc3Fxo3769sGjRIiEwMPCzx/z8888CACEoKOizjxsTEyPo6ekJ27dvFwRBEI4ePSpIJBIhODhYmD59ugBAiIyM/GJ8WlpawvXr17Psk8lkgiAIwqhRowQAwoULF+T73r59Kzg6OgplypQRMjMzBUH49/l2c3MTUlNT5X2XL18uABD++ecfeVtO8QEQpk+fniUWBwcHoVevXvKfK1WqJH/dc/LxMT66ffu2AEDo37+/Qr+xY8cKAIQzZ84oPB4A4c8//5S3RURECFKpVBgzZsxnHzcoKEgAIFhbWwtxcXHydh8fHwGAUKlSJSE9PV3e/sMPPwh6enpCSkqKvC0pKSnLuIMGDRIMDQ0V+rVu3VpwcHDI0vfja1G2bNksY33cd/bsWXlbZmamUK9ePcHW1laIiooShg4dKujo6GT7e0FEhRunvKnIS0hIAAAUK1bsq46fMmUKMjIycj2NXbZsWfTo0QMbNmxAaGjoVz3ml/j5+WHVqlVwdHTEgQMHMHbsWLi5uaFp06bZXmWuDHNzc7Ro0QK7du0C8P4CjTp16sgro18ik8lw8OBBfP/996hevXqW/R+ni48dO4YaNWqgXr168n3GxsYYOHAggoOD8eDBA4Xj+vTpo1AJq1+/PoD30+Z5xczMDPfv38eTJ09yfcyxY8cAAN7e3grtY8aMAYAsU/gVKlSQxw68n553cXHJ9Xl06tQJpqam8p9r1qwJAOjevbvC2s6aNWsiLS1N4ffBwMBA/v9v375FVFQU6tevj6SkJDx8+DBXjw8AvXr1UhgrJ1paWti6dSsSExPRsmVLrFmzBj4+Ptn+XhBR4caEkoo8ExMTAO//Af0aX5MgfikJjYmJQVhYmHyLj49XKiYtLS0MHToUN2/eRFRUFH7//Xe0bNkSZ86cQdeuXZUaKzs//vgjAgICEBISgoMHD+LHH3/M9bGRkZFISEj44nrLFy9ewMXFJUu7m5ubfP+nSpcurfCzubk5ACA2NjbXsX3JrFmzEBcXh/Lly8PDwwPjxo3D3bt3P3vMixcvoKWlleWqcjs7O5iZmX3xPID355Lb8/jv8R+Ty1KlSmXb/um49+/fR/v27WFqagoTExNYW1uje/fuAKDU76Cjo2Ou+5YrVw4zZszA9evXUbFiRUydOjXXxxJR4cGEkoo8ExMT2Nvb4969e189xuTJk5GRkYEFCxbkqn/ZsmXRvXv3HJPQDh06oHjx4vLtvxcEKcPS0hJt2rTBsWPH0LBhQ1y8eDFLEqOsNm3aQCqVolevXkhNTUXnzp1FjZcXtLW1s20XBOGrx/z0YhTg/a1vnj17hi1btsDd3R2bNm1C1apVsWnTpi+OldubnYs9j5yO/9K4cXFxaNiwIe7cuYNZs2bh8OHDCAgIkP9Oy2SyXD0+gFxVJz916tQpAO/X1kZHRyt1LBEVDkwoSSN89913ePbsGS5fvvxVx5crVw7du3fH+vXrla5SZpeELl68GAEBAfJt/PjxXxXXf32cShQ71W5gYIB27drh3LlzaNasmcJ9K7/E2toaJiYmX0zgHRwc8OjRoyztH6deczvFnhvm5uaIi4tTaEtLS8v2ebKwsECfPn2wa9cuvHz5Ep6entleIf6Rg4MDZDJZlmny8PBwxMXF5el5iHHu3DlER0dj69atGDlyJL777jt4eXnJK72fystvAlq3bh0CAgIwd+5cpKWlYdCgQXk2NhEVHEwoSSOMHz8eRkZG6N+/P8LDw7Psf/bsGZYvX/7ZMaZMmYL09HQsXLgwV4/5aRIaFhamsK9atWrw8vKSbxUqVMj1uYSFhWVZXwi8T5BOnz6d7fTr1xg7diymT5+u9BSllpYW2rVrh8OHD+PGjRtZ9n+smLVq1QrXrl1TSPLfvXuHDRs2oEyZMko9J19Srlw5/PnnnwptGzZsyFKh/G/1zNjYGE5OTllu//OpVq1aAUCWb5ZZsmQJAKB169ZfG3ae+ljB/LQSmpaWhjVr1mTpa2RkpPQyjOwEBQVh3Lhx6NixIyZNmoRFixbh0KFDubpNEhEVLrxtEGmEcuXKYefOnejSpQvc3NwUvinn0qVL2Lt3r8L9CHMao3v37vD398/1406ePBnbt2/Ho0ePcnVLmI9u3LiBOXPmZGlv1KgR9PX1UaNGDTRp0gRNmzaFnZ0dIiIisGvXLty5cwejRo1SqqKYk0qVKqFSpUpfdey8efNw6tQpNGzYEAMHDoSbmxtCQ0Oxd+9eXLx4EWZmZpg4cSJ27dqFli1bYsSIEbCwsIC/vz+CgoKwb98+aGnl3d+7/fv3x+DBg9GxY0c0a9YMd+7cwcmTJ7M8TxUqVECjRo1QrVo1WFhY4MaNG/jtt98++1WYlSpVQq9evbBhwwb5tPK1a9fg7++Pdu3aoXHjxnl2HmLUqVMH5ubm6NWrF0aMGAGJRILt27dnO9VerVo1/Prrr/D29sY333wDY2NjfP/990o9niAI6Nu3LwwMDLB27VoAwKBBg7Bv3z6MHDkSXl5eKrv5PxHlPyaUpDHatGmDu3fv4ueff8bvv/+OtWvXQiqVwtPTE4sXL8aAAQO+OMaUKVPwyy+/ZKls5cTJyUnpJBQArl69Kr+/4qdmz56NkSNHYtmyZTh27BjWrFmD8PBw6Ovrw93dHRs3bkS/fv2UeixVKFGiBK5evYqpU6dix44dSEhIQIkSJdCyZUv5fTNtbW1x6dIlTJgwAStXrkRKSgo8PT1x+PDhPK/qDRgwAEFBQdi8eTNOnDiB+vXrIyAgAE2bNlXoN2LECBw6dAinTp1CamoqHBwcMGfOHIwbN+6z42/atAlly5bF1q1bceDAAdjZ2cHHx0d+o/WCwNLSEkeOHMGYMWMwZcoUmJubo3v37mjatCmaN2+u0Penn37C7du34efnh6VLl8LBwUHphHLlypU4d+4c9u3bB2tra3n75s2b4e7ujgEDBmS5Ap6ICi+JIGZFOxERERFpPK6hJCIiIiJRmFASERERkShMKImIiIhIFCaURERERCQKE0oiIiIiEoUJJRERERGJwoSSiIiIiEQpkjc23/n3K3WHQB/su531aw5JfXb0rKbuEIgKnKi3aeoOgT4oaa6ntsc2qJLzN2KJlXxrlcrGLihYoSQiIiIiUYpkhZKIiIhIKRLW2MRgQklEREQkkag7gkKN6TgRERERicIKJRERERGnvEXhs0dEREREorBCSURERMQ1lKKwQklEREREorBCSURERMQ1lKLw2SMiIiIiUVihJCIiIuIaSlGYUBIRERFxylsUPntEREREJAorlERERESc8haFFUoiIiIiEoUVSiIiIiKuoRSFzx4RERERicIKJRERERHXUIrCCiURERERicIKJRERERHXUIrChJKIiIiIU96iMB0nIiIiIlFYoSQiIiLilLcofPaIiIiISBRWKImIiIhYoRSFzx4RERERicIKJREREZEWr/IWgxVKIiIiIhKFFUoiIiIirqEUhQklEREREW9sLgrTcSIiIiIShRVKIiIiIk55i8Jnj4iIiIhEYYWSiIiIiGsoRWGFkoiIiIhEYYWSiIiIiGsoReGzR0RERESisEJJRERExDWUojChJCIiIuKUtyh89oiIiIhIFFYo88H1gEO4EXAIcVHhAACbkg5o0KEHnCvXVOgnCAJ2LvDB0zvX0cV7Jly/qZfteJkZGTizZwue3r6G2IhQSA2MUNajKry69kcxCyuVn09h1rlKcXSpYq/Q9jouBSP235f/XN7aCD9Ws4eztRFkAhAck4TZJ58gLVPIdsz2nnao5WCGEmb6SMuQ4VHEO2y//gpvElJVei5F1e6dO+DvtxlRUZEo7+KKiZOmwsPTM8f+p04ex+qVy/Hm9WuUdiiDUd5jUb9Bw3yMuOjia1EwJL17B78Nq3Dx/GnExcbAqbwrho6eCNcK7jkek5aWhu2b1+GPk0cQGx0FC0tr9Og3GC2/b5+PkRcynPIWhQllPjCxsILXDwNgYVcCgIDbf57C7kXTMMh3PWxKlZH3u3J8X65+odPTUhAW9AQN2neHrUM5pLx7ixP+q7Fr0VQMnLdWdSdSRITEJmPmicfynzNl/yaK5a2NMKW5Mw7cDcXmKy+RKRNQxtIQsuxzSQBARTtjnAiMxNOod9DSkqBbtRKY1sIZI/c/QGqGTJWnUuScOH4Mixb6Ysr0mfDwqIQd2/0xZFA//H7kBCwtLbP0v33rb0wcNwYjRnmjQcPGOHb0MEYNH4rdv+2Hs3N5NZxB0cHXouBYPG86gp4/hc/0ebC0ssEfJ45g/PAB2LzrIKxtbLM9ZvbkMYiNicHYSTNRomRpREdHQvjcBxmRSJzyzgcu1erAuUpNWBYvCcvipdC0Sz/o6Rvg1dMH8j5hwU9x+ehetB007ovj6Rsao8fkn1GxdiNY2ZdCSecKaNlnOEKDHiP+QxWUcpYpExCXnCHf3qZmyvf1qVkSxx5E4MDdcLyMS8GbhFRcCopFxmc+iOeceoqzT6PxMi4FL2KSsepCMKyNpShnaZgfp1OkbPf3Q4f/dUa79h1RzskJU6bPhL6+Pg7u35dt/x2/bEOdevXRu29/lC1XDsNGjIJbhQrYvfOXfI686OFrUTCkpqTgz3N/YOAwb3hWqY4SpUqj14CfYF+yFA7v/zXbY65dvog7t25i3pI1qFajNuzsS6CiR2W4V6qSz9EXMhIt1W0aQDPOsgCRyTJx79IZpKemoJRzBQBAemoK9q2ai1Z9RsDYzOKrxk1NegdIJNA3NM7LcIuk4iZSbOzqgTWd3DGyYRlYGekCAEz0dVDexhjxyemY29oFm3/wxKyW5eFqa6TU+Ia62gCAt6kZeR57UZaelobAB/dRq3YdeZuWlhZq1aqDu3duZXvM3du3UatWbYW2OnXr4e7t26oMtcjja1FwZGZmQpaZCT09PYV2qVQf93J4LS5fOAcX1wr49Zct6Px9U/Ts9B3WrViE1JSUfIiYNJVap7yfP38OR0dHSESsW0hNTUVqquJatfS0VOjqScWGl6fCQ55j87ThyEhPg56+Abp4z4R1yTIAgBPb16BU+YpwrV73q8bOSEvDH7s2wqNOE0gNlUt+NM2TyHdYdSEYb+JTYW6oi06Vi2NOaxeM2v8AtsXe/850qWIP/+uvEBydhIZOlpjRojxGH3iA0FysiZTgfZUzMDwRL+P44a2M2LhYZGZmZplOtbS0RFDQ82yPiYqKgqWlVZb+UdFRKotTE/C1KDgMjYxQwaMSftmyHqXLlIW5hSXOnDqGB/fuwL5k6WyPCX3zCv/cvQVdqRSz5i9DfHwsli+ci4T4OIyfOiefz6AQ4RpKUdRaoXR2dkZkZKT85y5duiA8XLkpW19fX5iamipsh/xW53WoolnZl8Lg+RvQf/ZqVPdqg4NrFyDyVTAe3biE4Pu30aLn0K8aNzMjA3uXz4IgCGjdd2QeR1303HqVgMvBcXgRm4zbrxMwN+ApDPV0UNfRHFofPktOPYrE2SfRCIpJxtZrr/AmPgVNnLOuGcvOgNqlUdrcAEvOZv+PLhGRsnym+0KAgC7fN0WLBtVwYO9ONG7WElo5JEAymQwSiQSTZs6Ha0UP1KzTAENGjsOpY4dYpSSVUWuFUhAU16UdO3YMvr6+So3h4+MDb29vhbYDDyJz6K0+2jq6Hy7KAezLlseb549w5cR+6OpKERP+BvP7tVHov2fpTJR29UDvaUtyHDMzIwO/LZ+F+Khw9JyyiNXJr5CUlonQ+BTYmUjxT+hbAMCr/1QWX8WlwMpYL7vDFfSvVQrVSpli6rFHiElKV0m8RZm5mTm0tbURHR2t0B4dHQ0rq+zvXmBlZYXo/1TAoqOjYWXJux2IwdeiYLEvWQpL125FcnISkt69g6WVNWZPHoviJUpm29/CyhpW1jYwNi4mbytdpiwEQUBkRDhKlnbIr9ALFw1Z66gqhf7Zk0qlMDExUdgK2nR3dgSZDJnp6ajX9gcMWbARg+dvkG8A0LznELQdnPMFOh+Tyeiw1+gx+WcYFjPNr9CLFH0dLdiaSBGbnI6IxDREv0uDvam+Qp/ipvqITEz77Dj9a5VCDQczzDjxGBFf6EvZ09XTg1uFirh65bK8TSaT4erVy/DM4WICz8qVcfXKFYW2K5cvwbNyZVWGWuTxtSiYDAwMYWlljbcJ8bh+9RLqNGicbT93z8qIjoxEclKSvO3Vy2BoaWnleFU4gRfliKTWs5RIJFnWT4pZT1lQ/bFrE14E3kVcZBjCQ57jj12bEBx4Bx51m8LYzAI2pRwVNgAwtbSBuU1x+RirxvRG4PWLAD5Mcy+biTfPH6PDsEkQZDIkxsUgMS4GmRmsjH1Oz29KoIKdMayN9eBiY4TxTctBJhNw8XksAOD3f8LRqoINapUxg10xKbpWtUcJU32cfvxv5WV6C2e0dLOW/zygdik0KGeBZeeDkJyeCTMDHZgZ6EBPu+j9Lqtaj159sP+3PTh08ACeP3uGObNmIDk5Ge3adwAATPYZj+VLF8v7d+veE5f+ugD/rVsQ9PwZ1q5eifv37qHrj93VdAZFB1+LguP6lb9w7fJFhL55hRtXL2HM0H4o7eCIFt+1AwBsWrMM82dOkvdv+m1rmJiaYuGcKQgOeoa7t25g/colaPFde0j19XN4FCJx1D7l3bt3b0il7yuKKSkpGDx4MIyMFKdu9+/fr47w8sy7hFgcWDMfiXExkBoawbZ0WXSfOB/lPKvneozoNy+RmpQIAHgbG4VHNy8BANZPHKjQr9fUxShToXKexV7UWBrpYXQjRxST6iAhJQOB4YnwOfIQCSnvr8g++iACejoS9KlRCsZSbQTHJGPWyccIf/tv1dGumBTF9P9967RwswEAzG7lovBYq/4MxtmnilOG9HktWrZCbEwM1qxagaioSLi4umHN+k2w/DDNGhYaCq1P/tqvXKUqfBcuwqoVy7By2RKUdiiDZStX876HeYCvRcHxLvEtNq1djqiIcBQzMUX9xl7oO3gEdHTe36EiOioSEWGh8v4GhoZYuGIDVi72xU+9u8LE1BQNmzZH30HD1XUKhUMRLGjlJ4nw34WM+ahPnz656ufn56fUuDv/fvU14ZAK7LvN+2IWJDt6VlN3CEQFTtRbLlMpKEqaf3m9uqoYtFHdF4MkHxqisrELCrVWKJVNFImIiIhUQkPWOqoKnz0iIiIiEoXf5U1ERETENZSisEJJRERERKKwQklERETENZSiMKEkIiIi4pS3KEzHiYiIiEgUViiJiIhI4xXFb+rLT6xQEhEREZEorFASERGRxmOFUhxWKImIiIhIFFYoiYiIiFigFIUVSiIiIiIShRVKIiIi0nhcQykOE0oiIiLSeEwoxeGUNxERERGJwgolERERaTxWKMVhhZKIiIiIRGGFkoiIiDQeK5TisEJJRERERKKwQklERETEAqUorFASERERkSisUBIREZHG4xpKcVihJCIiIiJRWKEkIiIijccKpThMKImIiEjjMaEUh1PeRERERAVEZmYmpk6dCkdHRxgYGKBcuXKYPXs2BEGQ9xEEAdOmTUPx4sVhYGAALy8vPHnyRI1RM6EkIiIigkQiUdmmjAULFmDt2rVYtWoVAgMDsWDBAixcuBArV66U91m4cCFWrFiBdevW4erVqzAyMkLz5s2RkpKS109LrnHKm4iIiKiAuHTpEtq2bYvWrVsDAMqUKYNdu3bh2rVrAN5XJ5ctW4YpU6agbdu2AIBt27bB1tYWBw8eRNeuXdUSNyuURERERBLVbampqUhISFDYUlNTsw2jTp06OH36NB4/fgwAuHPnDi5evIiWLVsCAIKCghAWFgYvLy/5MaampqhZsyYuX76cl8+IUphQEhEREamQr68vTE1NFTZfX99s+06cOBFdu3aFq6srdHV1UaVKFYwaNQrdunUDAISFhQEAbG1tFY6ztbWV71MHTnkTERGRxlPlVd4+Pj7w9vZWaJNKpdn23bNnD3bs2IGdO3eiYsWKuH37NkaNGgV7e3v06tVLZTGKxYSSiIiISIWkUmmOCeR/jRs3Tl6lBAAPDw+8ePECvr6+6NWrF+zs7AAA4eHhKF68uPy48PBwVK5cOc9jzy1OeRMREZHGKyhXeSclJUFLSzE909bWhkwmAwA4OjrCzs4Op0+flu9PSEjA1atXUbt2bfFPxFdihZKIiIg0XkG5sfn333+PuXPnonTp0qhYsSJu3bqFJUuWoG/fvgDexzlq1CjMmTMHzs7OcHR0xNSpU2Fvb4927dqpLW4mlEREREQFxMqVKzF16lT89NNPiIiIgL29PQYNGoRp06bJ+4wfPx7v3r3DwIEDERcXh3r16uHEiRPQ19dXW9wS4dNbrxcRO/9+pe4Q6IN9t8PVHQJ9YkfPauoOgajAiXqbpu4Q6IOS5npqe2ybfntUNnbE5s4qG7ug4BpKIiIiIhKFU95ERESk8QrKGsrCihVKIiIiIhKlSFYoO3iWVHcI9EG/fvPVHQJ9imsoC4yit3q98DI30lV3CFQAsEIpDiuURERERCRKkaxQEhERESmDFUpxmFASERGRxmNCKQ6nvImIiIhIFFYoiYiIiFigFIUVSiIiIiIShRVKIiIi0nhcQykOK5REREREJAorlERERKTxWKEUhxVKIiIiIhKFFUoiIiLSeKxQisOEkoiIiIj5pCic8iYiIiIiUVihJCIiIo3HKW9xWKEkIiIiIlFYoSQiIiKNxwqlOKxQEhEREZEorFASERGRxmOFUhxWKImIiIhIFFYoiYiISOOxQikOE0oiIiIi5pOicMqbiIiIiERhhZKIiIg0Hqe8xWGFkoiIiIhEYYWSiIiINB4rlOKwQklEREREorBCSURERBqPBUpxWKEkIiIiIlFYoSQiIiKNxzWU4jChJCIiIo3HfFIcTnkTERERkSisUBIREZHG45S3OKxQEhEREZEorFASERGRxmOBUhxWKImIiIhIFFYoiYiISONpabFEKQYrlEREREQkCiuUREREpPG4hlIcJpRERESk8XjbIHE45U1EREREorBCqUa7d+6Av99mREVForyLKyZOmgoPT89s+z59+gRrVq5A4IP7ePPmNcZN8EH3nr3zN+AiwthQiuk/fYc2TSrB2twYdx69wtiFv+HmgxAAQPKtVdkeN2npASzddjrbfQM61cOA/9WHg70FACDweRjmbTiOU389UM1JFGHKvC/27d2Dw4cO4unTJwCAChUqYvhI7xz7k3J273r/WkR/eC0mTJoKD4+cn9uEhASsWrEUZ/4IQHx8HIrbl8C4CZNQv0HDfIy66Ll54zq2bd2MwAf3ERUZicXLVqFxU68c+0+fPBGHDx3M0l62nBN+O3hEhZEWbixQisMKpZqcOH4Mixb6YtBPQ7F77wG4uLhiyKB+iI6OzrZ/SnIySpYqiRGjx8DKyjqfoy1a1k77EU1quaLvFH9U7zwPf1x+iKPrhsPe2hQAUMbLR2EbOP0XyGQyHDh9O8cxX4fHYerK31Gn20LU7fYzzl17jL1LB8KtrF0+nVXRoOz74sb1q2jZqjU2bdmG7Tt2w9auOIYM7Ivw8PB8jrzoOXn8GBYv9MWgIUOxa+8BlHdxxU+D+iEmh9ciPT0Ngwf0wZvXr/HzkuU4eOQEps2YDRsb23yOvOhJSU5G+fKumDh5Wq76j504GafOXpBvxwPOwdTUFF7fNldxpKTJJIIgCOoOIq+lZKg7gi/r1rUTKrp7YNKU9x8QMpkM3zZtiB9+7IF+AwZ+9tiWzZqgW4+ehaJCaf7NMHWHoEBfqovIi4vQafQGnLh4X97+147xOPXXA8xck/Wv9z1LBsDYUB+tBq9U6rFen1uAScsOwv/gZdFx55XY69lXXwsKMe8LAMjMzET92t/AZ/I0fN+2nYqjFaegf/J2/+H9a+Ez+d/XornX+9eib/+sr8XeX3fB328zDhw+Dl1d3fwOVxRZQX8xPlHVw/WLFcr/Onv6D4wdPRyHT/wBe/sSKoxOPCM99ZUJPaf9obKx787K/etVWLFCqQbpaWkIfHAftWrXkbdpaWmhVq06uHvnlhojK/p0tLWgo6ONlLR0hfaU1HTUqVIuS38bi2JoUc9dqaRQS0uCTs2rwchAD1fvBomOWVPkxfsiJSUZGRkZMDE1VVWYGiE9/f1rUbOW4mtR8zOvxblzZ+BZqTJ8585CkwZ10LHdd9i0YR0yMzPzK2zKwcEDv6FmrdoFPpmkwk2tayi1tLS+eFWVRCJBRkbOJcfU1FSkpqYqtAnaUkil0jyJURVi42KRmZkJS0tLhXZLS0sEBT1XU1SaITEpFVfuPIfPgJZ4FBSO8OgEdG5RHTU9HfHsZWSW/t2/r4m3SSk4eOb2F8eu6GSPc/5joK+ng8TkVHQZsxEPn4ep4CyKprx4XyxbvAjWNjYKSSkpLzY259ciOIfX4vWrl7j++gpatf4eq9ZuwMuQEMybMxMZGRkY/FPBmqnQJJER4bh08QLmzl+k7lAKPF7lLY5aE8oDBw7kuO/y5ctYsWIFZDLZZ8fw9fXFzJkzFdomT52OKdNm5EWIVAT1nbIN62d0w/NTc5GRkYnbD19iz4kbqOJWOkvfnm1r4dfjN5Ca9uV1FI+Dw1Gzqy9MjQ3Q3qsKNs7qgW/7L2dSmU82b9yAE8ePYfPWbQX6D8qiSiYTYGFhiakzZkNbWxsVKrojIiIc/n6bmVCq0eFDB1GsWDE0btpU3aFQEafWhLJt27ZZ2h49eoSJEyfi8OHD6NatG2bNmvXZMXx8fODt7a3QJmgX7H9MzM3Moa2tneVCg+joaFhZWakpKs0R9CoK3/ZfDkN9PZgY6yMsKgHb5/dB0OsohX51q5SDi6Mdekz0y9W46RmZeP7y/Ri3Al+iWsXSGPpDIwyfuzvPz6EoEvO+8PfbDL/NG7B+kx/Ku7iqMkyNYG6u/GthbW0NHR0daGtry9scy5ZFVFQk0tPToKurp9KYKStBEPD7gf1o9V1bPv+5wAKlOAVmDeWbN28wYMAAeHh4ICMjA7dv34a/vz8cHBw+e5xUKoWJiYnCVtCrE7p6enCrUBFXr/y7Lk8mk+Hq1cvwrFRFjZFplqSUNIRFJcCsmAG86rjhyLl/FPb3alcbNx+E4J/Hr79qfC2JBFI93pkrt772feG3eSM2rFuDNes3oaK7R36EWuTp6r5/La5dVXwtrn3mtahUuSpCQkIUZpVeBAfD2tqayYya3LxxDS9DXqBdh47qDqVQkEgkKts0gdoTyvj4eEyYMAFOTk64f/8+Tp8+jcOHD8Pd3V3doalUj159sP+3PTh08ACeP3uGObNmIDk5Ge3adwAATPYZj+VLF8v7p6el4WFgIB4GBiI9PQ0REeF4GBiIkBcv1HQGhZdXbTc0q+MGB3tLNKnpihMbR+JxUDi2Hfr3H89iRvro0KwKth64lO0Yx9YNx+AuDeQ/zxreBnWrlkPp4hao6GSPWcPboEF1Z+w+dkPl51OUKPu+2LJpA1avXI6Zs+fB3r4EoiIjERUZiaR379R0BkVHj54fXovf378Wc2e/fy3atnv/WkzxGY8Vn7wWnbv8gIT4OCycPxcvgoPw5/lz2LxxPTp37aauUygykpLe4dHDQDx6GAgAeP36FR49DERo6BsAwMplizF10oQsxx3cvw/unpXg5Fw+X+MlzaTW8snChQuxYMEC2NnZYdeuXdlOgRdVLVq2QmxMDNasWoGoqEi4uLphzfpNsPwwnRQWGgotyb/5fkRkBLr8r538Z3+/LfD324Lq39TA5q3b8zv8Qs3UWB+zhrdBCVszxMQn4ffTtzF99WFkZPxbWenUvBokkGDPiewTwrKlrGBpZiz/2drCGJtn94SdlQniE1Nw78lrfP/TGpy5+lDl51OUKPu+2PvrbqSnp2PM6BEK4wz+aRiGDB2er7EXNc1btkJsbAzWfvparPv3tQgNDYVE69/Xwq54caxZvxmLFvqiU4c2sLGxxY/de6JPvwHqOoUi48H9exjYt5f85yU/zwcAfN+mHWbOnY+oyEiEfUguP3r79i3O/HEKYydMytdYCzMNKSSqjFrvQ6mlpQUDAwN4eXkprLv5r/379ys1bmG4D6WmKGj3odR0Bf0+lJqkEN36sMgrTPehLOrUeR/KqrPOqGzsv6c1UdnYBYVaK5Q9e/bUmLUFREREVHAxHxFHrQnl1q1b1fnwRERERJQHeAkqERERaTwWKMVR+1XeRERERFS4sUJJREREGo9rKMVhhZKIiIiIRGGFkoiIiDQeC5TiMKEkIiIijccpb3E45U1EREREorBCSURERBqPBUpxWKEkIiIiIlFYoSQiIiKNxzWU4rBCSURERESisEJJREREGo8FSnFYoSQiIiIiUVihJCIiIo3HNZTiMKEkIiIijcd8UhxOeRMRERGRKKxQEhERkcbjlLc4rFASERERkSisUBIREZHGY4VSHFYoiYiIiEgUViiJiIhI47FAKQ4rlEREREQkCiuUREREpPG4hlIcJpRERESk8ZhPisMpbyIiIiIShRVKIiIi0nic8haHFUoiIiIiEoUJJREREWk8iUR1m7Jev36N7t27w9LSEgYGBvDw8MCNGzfk+wVBwLRp01C8eHEYGBjAy8sLT548ycNnQ3lMKImIiIgKiNjYWNStWxe6uro4fvw4Hjx4gMWLF8Pc3FzeZ+HChVixYgXWrVuHq1evwsjICM2bN0dKSora4uYaSiIiItJ4WgVkDeWCBQtQqlQp+Pn5ydscHR3l/y8IApYtW4YpU6agbdu2AIBt27bB1tYWBw8eRNeuXfM9ZoAVSiIiIiKVSk1NRUJCgsKWmpqabd9Dhw6hevXq6NSpE2xsbFClShVs3LhRvj8oKAhhYWHw8vKSt5mamqJmzZq4fPmyys8lJ0woiYiISOOpcg2lr68vTE1NFTZfX99s43j+/DnWrl0LZ2dnnDx5EkOGDMGIESPg7+8PAAgLCwMA2NraKhxna2sr36cOnPImIiIijafK2wb5+PjA29tboU0qlWbbVyaToXr16pg3bx4AoEqVKrh37x7WrVuHXr16qSxGsVihJCIiIlIhqVQKExMThS2nhLJ48eKoUKGCQpubmxtCQkIAAHZ2dgCA8PBwhT7h4eHyferAhJKIiIg0npZEdZsy6tati0ePHim0PX78GA4ODgDeX6BjZ2eH06dPy/cnJCTg6tWrqF27tujn4WtxypuIiIiogBg9ejTq1KmDefPmoXPnzrh27Ro2bNiADRs2AHg/NT9q1CjMmTMHzs7OcHR0xNSpU2Fvb4927dqpLW4mlERERKTxCspXL37zzTc4cOAAfHx8MGvWLDg6OmLZsmXo1q2bvM/48ePx7t07DBw4EHFxcahXrx5OnDgBfX19tcUtEQRBUNujq0hKhrojoI/Mvxmm7hDoE7HXV6k7BPqg6H3yFl4yvhgFhpGe+pK6VuuuqWzsY4NrqGzsgoIVSiIiItJ4BaRAWWgxoSSVenlhmbpDoE+Yt/pZ3SHQB7HHxqk7BPogLV2m7hDoAyM9bXWHQF+JCSURERFpPAlYohSDCSURERFpPGVv70OKeB9KIiIiIhKFFUoiIiLSeAXltkGFFSuURERERCQKK5RERESk8VigFIcVSiIiIiIShRVKIiIi0nhaLFGKwgolEREREYnChJKIiIg0nkSiuq2giouLw6ZNm+Dj44OYmBgAwN9//43Xr18rPRanvImIiEjjadptg+7evQsvLy+YmpoiODgYAwYMgIWFBfbv34+QkBBs27ZNqfFylVDevXs31wN6enoqFQARERER5S9vb2/07t0bCxcuRLFixeTtrVq1wo8//qj0eLlKKCtXrgyJRAJBELLd/3GfRCJBZmam0kEQERERqZOGFShx/fp1rF+/Pkt7iRIlEBYWpvR4uUoog4KClB6YiIiIiAomqVSKhISELO2PHz+GtbW10uPlKqF0cHBQemAiIiKiwkLTbhvUpk0bzJo1C3v27AHwfrY5JCQEEyZMQMeOHZUe76uu8t6+fTvq1q0Le3t7vHjxAgCwbNky/P77718zHBERERHlo8WLFyMxMRE2NjZITk5Gw4YN4eTkhGLFimHu3LlKj6d0Qrl27Vp4e3ujVatWiIuLk6+ZNDMzw7Jly5QOgIiIiEjdJCrcCiJTU1MEBATg8OHDWLFiBYYNG4Zjx47h/PnzMDIyUno8pW8btHLlSmzcuBHt2rXD/Pnz5e3Vq1fH2LFjlQ6AiIiIiNSjXr16qFevnuhxlE4og4KCUKVKlSztUqkU7969Ex0QERERUX7TtPtQrlixItt2iUQCfX19ODk5oUGDBtDW1s7VeEonlI6Ojrh9+3aWC3VOnDgBNzc3ZYcjIiIiUjstzconsXTpUkRGRiIpKQnm5uYAgNjYWBgaGsLY2BgREREoW7Yszp49i1KlSn1xPKXXUHp7e2Po0KH49ddfIQgCrl27hrlz58LHxwfjx49X/oyIiIiIKF/NmzcP33zzDZ48eYLo6GhER0fj8ePHqFmzJpYvX46QkBDY2dlh9OjRuRpP6Qpl//79YWBggClTpiApKQk//vgj7O3tsXz5cnTt2lXpEyIiIiJSN02b8p4yZQr27duHcuXKyducnJywaNEidOzYEc+fP8fChQtzfQuhr/ou727duqFbt25ISkqSX3JORERERIVDaGgoMjIysrRnZGTIvynH3t4eb9++zdV4X3UfSgCIiIjAzZs38ejRI0RGRn7tMERERERqJ5GobiuIGjdujEGDBuHWrVvytlu3bmHIkCFo0qQJAOCff/6Bo6NjrsZTOqF8+/YtevToAXt7ezRs2BANGzaEvb09unfvjvj4eGWHIyIiIqJ8tnnzZlhYWKBatWqQSqWQSqWoXr06LCwssHnzZgCAsbExFi9enKvxvmoN5a1bt3D06FHUrl0bAHD58mWMHDkSgwYNwu7du5UdkoiIiEitNG0NpZ2dHQICAvDw4UM8fvwYAODi4gIXFxd5n8aNG+d6PKUTyiNHjuDkyZMKN8Fs3rw5Nm7ciBYtWig7HBERERGpiaurK1xdXUWPo3RCaWlpCVNT0yztpqam8vsYERERERUmmnYfSgB49eoVDh06hJCQEKSlpSnsW7JkiVJjKZ1QTpkyBd7e3ti+fTvs7OwAAGFhYRg3bhymTp2q7HBEREREaqdpU96nT59GmzZtULZsWTx8+BDu7u4IDg6GIAioWrWq0uPlKqGsUqWKwhP95MkTlC5dGqVLlwYAhISEQCqVIjIyEoMGDVI6CCIiIiLKPz4+Phg7dixmzpyJYsWKYd++fbCxsUG3bt2+agljrhLKdu3aKT0wERERUWGhWfVJIDAwELt27QIA6OjoIDk5GcbGxpg1axbatm2LIUOGKDVerhLK6dOnKx8pERERERVIRkZG8nWTxYsXx7Nnz1CxYkUAQFRUlNLjfdU35RAREREVJVoatoayVq1auHjxItzc3NCqVSuMGTMG//zzD/bv349atWopPZ7SCWVmZiaWLl2KPXv2ZHtVUExMjNJBEBEREVH+WbJkCRITEwEAM2fORGJiIn799Vc4OzsrfYU38BUJ5cyZM7Fp0yaMGTMGU6ZMweTJkxEcHIyDBw9i2rRpSgdAREREpG4aVqBE2bJl5f9vZGSEdevWiRpP6a9e3LFjBzZu3IgxY8ZAR0cHP/zwAzZt2oRp06bhypUrooIhIiIiItUrW7YsoqOjs7THxcUpJJu5pXRCGRYWBg8PDwDvv+Px4/d3f/fddzh69KjSARARERGpm0QiUdlWEAUHByMzMzNLe2pqKl6/fq30eEpPeZcsWRKhoaEoXbo0ypUrh1OnTqFq1aq4fv06pFKp0gEQERERUf44dOiQ/P9Pnjyp8O2HmZmZOH36NMqUKaP0uEonlO3bt8fp06dRs2ZNDB8+HN27d8fmzZsREhKC0aNHKx0AERERkboV0EJinvt4b3GJRIJevXop7NPV1UWZMmWwePFipcdVOqGcP3++/P+7dOkCBwcHXLp0Cc7Ozvj++++VDkCT7d65A/5+mxEVFYnyLq6YOGkqPDw9c+x/6uRxrF65HG9ev0ZphzIY5T0W9Rs0zMeIi57N61djy4Y1Cm2lHRyxa/+RbPsPG9gbt25ez9Jeu24DLFqxViUxFlVaWhJM6VEHPzStAFtzI4RGv8P2gHuYv+OyvI+NmSHm9G8Ir2plYGokxcV/XsF79R949iYux3G7N6uIjeNaKbSlpGXA/LulqjqVIoufUQXPti0bsWblUnT5sQdGj/PJsd/uHduwf+9uhIeFwtTMHE28vsWQ4aM5k/gZmnLbIJlMBgBwdHTE9evXYWVllSfjir4PZa1atVCrVi1ERERg3rx5mDRpUl7EVeSdOH4Mixb6Ysr0mfDwqIQd2/0xZFA//H7kBCwtLbP0v33rb0wcNwYjRnmjQcPGOHb0MEYNH4rdv+2Hs3N5NZxB0eFYzgnL12yS/6ytnfPbYt7Py5Ceni7/OT4+Hr1/6IDGXt+qNMaiaEznGhjwXWUM+Pk4HryIQrXydlg/piUS3qVizcG/AQB7ZrRHemYmOk0/gISkVIzo+A2OLeiMKgP8kJSSnuPY8e9SUanvZvnPgiCo/HyKGn5GFTwP7v+DA/v2wMnZ5bP9Th4/gjUrlmDyjDnwqFQFL18EY/a0SQAkGDV2Qv4ESwVeUFBQno6n9EU5OQkNDcXUqVNFjREVFYWEhIQ8iqhg2+7vhw7/64x27TuinJMTpkyfCX19fRzcvy/b/jt+2YY69eqjd9/+KFuuHIaNGAW3ChWwe+cv+Rx50aOtrQ1LK2v5ZmZunmNfE1Mzhb7Xr16CVF8fTZo1z8eIi4ZaFUrgyOWnOHHtOULCE3DgwmOcvhmM6i7FAQBOJcxRs4I9RqwIwM3HYXjyKhYjVpyCvlQHnRu5fnZsQRAQHvtOvkXEJeXHKRUp/IwqWJKS3mH6pPHwmToTxUxMPtv3nzu34Vm5Cpq3/A729iVQs3ZdNGvRCg/u/5NP0RZOEonqtoLq9OnTmDRpEvr374++ffsqbMrKs4Tya8XFxWHo0KGwsrKCra0tzM3NYWdnBx8fHyQlFc1/BNLT0hD44D5q1a4jb9PS0kKtWnVw986tbI+5e/s2atWqrdBWp2493L19W5WhaoRXISFo07wROrVpjhmTxyMs9E2ujz1ycD+8vm0JAwNDFUZYNF158BqNKzvAqcT7BN6jrDVqu5fAqevPAQBSXW0AQErav1chCgKQlp6JOu4lPzu2sYEeHm0fiCc7BmHPjHZwc8haUaOc8TOq4FnkOwd16zdEjVp1vtjXo1JlPHzwAPfv3QUAvH71Epf+uoA69eqrOkwqRGbOnIlvv/0Wp0+fRlRUFGJjYxU2Zan1qxdjYmJQu3ZtvH79Gt26dYObmxsA4MGDB1i5ciUCAgJw8eJF3L17F1euXMGIESPUGW6eiY2LRWZmZpZpI0tLSwQFPc/2mKioKFhaWmXpHxWt/Pdt0r8quHti8oy5KF2mDKIjI7Fl41r81L8ntu/5HUZGRp899sG9u3j+7Al8ps3Kp2iLlkW/XoWJoRR3NvdDpkwGbS0tTN96AbvPBAIAHr2MQUh4PGb3rY9hy0/hXUo6RnSojpLWJrCzyPm1efIqFoMWn8C955EwMdLDqE7f4Oyybqg2YAteRyXm1+kVavyMKlgCThzDo4cPsOWXPbnq37zld4iLjcWgPt0hAMjMyED7/3VB736DVBtoIVdQb++jKuvWrcPWrVvRo0ePPBlPrQnlrFmzoKenh2fPnsHW1jbLvm+//RY9evTAqVOnsGLFimzHSE1NRWpqqkKboC3lwmPKldp1//2L3cnZBRU8PNGxdTOcCTiB79t1/OyxR37fj3JO5VHBPeeLFChn/2voiq5N3dB7/hE8CI6CZzkb/DykCUKjE7Ej4D4yMmXoOut3rPVugdD9I5CRKcOZv1/gxLXnn51Cuhr4BlcD/60yX3nwBrc39UW/1pUwy/+vfDgzorwTHhaKJT/7YsXaTbn+d+3mjWvw37IB43ymoaKHJ169DMHSn+dhy4a16DtwiIojpsIiLS0Ndep8ueKdW7lOKL29vT+7PzIyUukHP3jwINavX58lmQQAOzs7LFy4EK1atcL06dOzXNr+ka+vL2bOnKnQNnnqdEyZNkPpePKLuZk5tLW1s9yhPjo6OserraysrBD9n7/0o6OjYWWZN1dn0XvFipmglIMDXr0M+Wy/5OQk/HHyOPoPHpZPkRU98wY0xKLd17D33EMAwP3gKJS2NcG4rjWxI+A+AODWk3DUGuIPE0M96OlqIyo+GX+u6Iabj8Nz/TgZmTLceRaBcvY5r40lRfyMKjgeBt5HbEw0ev/4P3lbZmYmbv99A7/9uhN/Xr0NbW1thWM2rFmBlq3boG2H98c4OZdHcnIS5s+Zgd79B0FLS+2r3QokTXtW+vfvj507d4q+/uWjXCeUt25lv27mUw0aNFDqwUNDQ1GxYsUc97u7u0NLSwvTp0/PsY+Pj0+WZFfQLtjVSV09PbhVqIirVy6jSVMvAO8v47969TK6/tA922M8K1fG1StX0L1nb3nblcuX4Fm5cj5ErDmSkt7h9auXaNGqzWf7nQk4ifT0NDRvxVtlfS0DqS5k/7n6OlMmZHvrjoSkNABAOXszVHW2w0wlKo1aWhJUdLTCyWt5e0VjUcbPqIKjeo3a2LH3d4W2OdMnw8HRET1698+STAJASkoKJP9JGrW13vfjHQ/oo5SUFGzYsAF//PEHPD09oaurq7B/yZIlSo2X64Ty7NmzSg2cG1ZWVggODkbJktkvsA8KCoKNjc1nx5BKs05vp2TkWYgq06NXH0ydNAEVK7rD3cMTv2z3R3JyMtq17wAAmOwzHjY2thg5egwAoFv3nujXuwf8t25BgwYNceL4Mdy/dw9TZ3D9nhirlv6Mug0awa64PaIiI7Bp/Wpoa2nDq8X7+xjOnuYDK2sbDBmueNP+I7/vR/1GTWFqZqaGqIuGY1eeYcIPtfAyIgEPXkShspMtRnSojm0n/70StUP98oiMT8bLiAS4O1pj0ZAmOHzpKU7fDJb32TSuFd5Ev8W0LRcAAD7dauPaw1A8ex0LM2MpRneqgdI2JvA7fje/T7FQ42dUwWBkZIRyTs4KbfoGBjA1NZO3z5wyEdY2NvhpxPviSr0GjbDrF3+4uLihoocnXr4MwYa1K1CvQaNsE1B6T9PWUN69exeVP/zBd+/ePYV9X/NcqHUNZfPmzTF58mQEBARAT09PYV9qaiqmTp2KFi1aqCk61WrRshViY2KwZtUKREVFwsXVDWvWb4Llh+mksNBQaEn+/QuzcpWq8F24CKtWLMPKZUtQ2qEMlq1czfu7iRQREY7pk8YhIT4OZuYW8KxcFeu37oS5uQWA9+uX/vvGehEchLu3/8bS1RvVEXKR4b36D0zvVQ/Lh3vB2swQodHvsPnYHcz75ZK8j52lMRYMbgwbMyOExSRixx/34fvJjc8BoJRNMYVKp3kxfawZ9S1szY0Qm5iKW0/C0HjUTjwMUZy+pc/jZ1ThERYWqlCR7NN/MCQSCdavWY7IiAiYmZujXoPGGDxspBqjLPi0NCufzPNCoURQY/371atXqF69OqRSKYYOHQpXV1cIgoDAwECsWbMGqampuH79OkqXLq3UuIWhQqkpEvliFCilOvDbYgqK2GPj1B0CfZD8ya2pSL3MDdVXQR31+0OVjb2s7efvnatOT58+xbNnz9CgQQMYGBhAEITCV6EsWbIkLl++jJ9++gk+Pj7ytR0SiQTNmjXDqlWrlE4miYiIiJSlaRXK6OhodO7cGWfPnoVEIsGTJ09QtmxZ9OvXD+bm5kp/n7faL2pydHTE8ePHERUVhStXruDKlSuIjIzEiRMn4OTkpO7wiIiIiIqc0aNHQ1dXFyEhITA0/PfLObp06YITJ04oPZ5aK5SfMjc3R40aNdQdBhEREWkgTbso59SpUzh58mSWC6OdnZ3x4sULpcf7qgrlhQsX0L17d/m33ADA9u3bcfHixa8ZjoiIiIjy0bt37xQqkx/FxMR81ZfDKJ1Q7tu3D82bN4eBgQFu3bol/5aa+Ph4zJs3T+kAiIiIiNRNS6K6rSCqX78+tm3bJv9ZIpFAJpNh4cKFaNy4sdLjKT3lPWfOHKxbtw49e/bE7t275e1169bFnDlzlA6AiIiIiPLXwoUL0bRpU9y4cQNpaWkYP3487t+/j5iYGPz1l/JfU6t0hfLRo0fZfiOOqakp4uLilA6AiIiISN0kEtVtBZG7uzseP36MevXqoW3btnj37h06dOiAW7duoVy5ckqPp3SF0s7ODk+fPkWZMmUU2i9evIiyZcsqHQARERGRumX3ta9FnampKSZPnpwnYyldoRwwYABGjhyJq1evQiKR4M2bN9ixYwfGjh2LIUOG5ElQRERERKQ6fn5+2Lt3b5b2vXv3wt/fX+nxlK5QTpw4ETKZDE2bNkVSUhIaNGgAqVSKsWPHYvjw4UoHQERERKRuar8xdz7z9fXF+vXrs7Tb2Nhg4MCB6NWrl1LjKZ1QSiQSTJ48GePGjcPTp0+RmJiIChUqwNjYWNmhiIiIiEgNQkJC4OjomKXdwcEBISEhSo/31Tc219PTQ4UKFb72cCIiIqICQ9OWUNrY2ODu3btZrom5c+cOLC0tlR5P6YSycePGn72b/JkzZ5QOgoiIiIjyzw8//IARI0agWLFi8rv3nD9/HiNHjkTXrl2VHk/phLJy5coKP6enp+P27du4d++e0vPtRERERAWBpl3lPXv2bAQHB6Np06bQ0XmfDspkMvTs2fOrvqhG6YRy6dKl2bbPmDEDiYmJSgdARERERPlHEASEhYVh69atmDNnDm7fvg0DAwN4eHjAwcHhq8b86jWU/9W9e3fUqFEDixYtyqshiYiIiPKFJhUoBUGAk5MT7t+/D2dnZzg7O4seM8+ukr98+TL09fXzajgiIiKifKNJ3+WtpaUFZ2dnREdH59mYSlcoO3TooPCzIAgIDQ3FjRs3MHXq1DwLjIiIiIhUY/78+Rg3bhzWrl0Ld3d30eMpnVCampoq/KylpQUXFxfMmjUL3377reiAiIiIiPKbpl2U07NnTyQlJaFSpUrQ09ODgYGBwv6YmBilxlMqoczMzESfPn3g4eEBc3NzpR6IiIiIiAqGZcuW5el4SiWU2tra+PbbbxEYGMiEkoiIiIoMDStQ5vmtHpW+KMfd3R3Pnz/P0yCIiIiIKH89e/YMU6ZMwQ8//ICIiAgAwPHjx3H//n2lx1I6oZwzZw7Gjh2LI0eOIDQ0FAkJCQobERERUWGjSVd5A++/FcfDwwNXr17F/v375fcSv3PnDqZPn670eLlOKGfNmoV3796hVatWuHPnDtq0aYOSJUvC3Nwc5ubmMDMz4zQ4ERERUSEwceJEzJkzBwEBAdDT05O3N2nSBFeuXFF6vFyvoZw5cyYGDx6Ms2fPKv0gRERERAWZBAW0lKgi//zzD3bu3Jml3cbGBlFRUUqPl+uEUhAEAEDDhg2VfhAiIiKigqygTk2ripmZGUJDQ+Ho6KjQfuvWLZQoUULp8ZRaQynRtEugiIiIiIqgrl27YsKECQgLC4NEIoFMJsNff/2FsWPHomfPnkqPp9Rtg8qXL//FpFLZG2ESERERqZumVSjnzZuHYcOGoXTp0sjIyECFChWQmZmJH3/8EVOmTFF6PKUSypkzZ2b5phwiIiIiKhxkMhl+/vlnHDp0CGlpaejRowc6duyIxMREVKlSBc7Ozl81rlIJZdeuXWFjY/NVD0RERERUUGnKsr65c+dixowZ8PLygoGBAXbu3AlBELBlyxZR4+Z6DaWmPNFERERERdW2bduwZs0anDx5EgcPHsThw4exY8cOyGQyUePmOqH8eJU3ERERUVGjKTc2DwkJQatWreQ/e3l5QSKR4M2bN6LGzXVCKZPJON1NRERElI/mz58PiUSCUaNGydtSUlIwdOhQWFpawtjYGB07dkR4eHiuxsvIyIC+vr5Cm66uLtLT00XFqdQaSiIiIqKiqCCu7Lt+/TrWr18PT09PhfbRo0fj6NGj2Lt3L0xNTTFs2DB06NABf/311xfHFAQBvXv3hlQqlbelpKRg8ODBMDIykrft379fqViZUBIREZHG0ypgGWViYiK6deuGjRs3Ys6cOfL2+Ph4bN68GTt37kSTJk0AAH5+fnBzc8OVK1dQq1atz47bq1evLG3du3cXHS8TSiIiIiIVSk1NRWpqqkKbVCpVqBL+19ChQ9G6dWt4eXkpJJQ3b95Eeno6vLy85G2urq4oXbo0Ll++/MWE0s/P7yvP4vOU+qYcIiIioqJIlRfl+Pr6wtTUVGHz9fXNMZbdu3fj77//zrZPWFgY9PT0YGZmptBua2uLsLCwvH5aco0VSiIiIiIV8vHxgbe3t0JbTtXJly9fYuTIkQgICMhy8UxBxoSSiIiINJ4ql1B+aXr7Uzdv3kRERASqVq0qb8vMzMSff/6JVatW4eTJk0hLS0NcXJxClTI8PBx2dnZ5HXquMaEkIiIiKiCaNm2Kf/75R6GtT58+cHV1xYQJE1CqVCno6uri9OnT6NixIwDg0aNHCAkJQe3atdURMgAmlERERETQQsG4yrtYsWJwd3dXaDMyMoKlpaW8vV+/fvD29oaFhQVMTEwwfPhw1K5d+4sX5KgSE0pSqUF77qg7BPpEzNFx6g6BPlhz6bm6Q6APBtQoo+4QiJSydOlSaGlpoWPHjkhNTUXz5s2xZs0atcbEhJKIiIg0XgG7DaWCc+fOKfysr6+P1atXY/Xq1eoJKBtMKImIiEjjFbTv3C5seB9KIiIiIhKFFUoiIiLSeAXtqxcLG1YoiYiIiEgUViiJiIhI47FAKQ4rlEREREQkCiuUREREpPG4hlIcViiJiIiISBRWKImIiEjjsUApDhNKIiIi0nicshWHzx8RERERicIKJREREWk8Cee8RWGFkoiIiIhEYYWSiIiINB7rk+KwQklEREREorBCSURERBqPNzYXhxVKIiIiIhKFFUoiIiLSeKxPisOEkoiIiDQeZ7zF4ZQ3EREREYnCCiURERFpPN7YXBxWKImIiIhIFFYoiYiISOOxwiYOnz8iIiIiEoUVSiIiItJ4XEMpDiuURERERCQKK5RERESk8VifFIcVSiIiIiIShRVKIiIi0nhcQykOE0oiIiLSeJyyFYfPHxERERGJwgolERERaTxOeYvDCiURERERicIKJREREWk81ifFYYWSiIiIiERhhZKIiIg0HpdQisMKJRERERGJwgolERERaTwtrqIUhQklERERaTxOeYvDhFKNdu/cAX+/zYiKikR5F1dMnDQVHp6eOfY/dfI4Vq9cjjevX6O0QxmM8h6L+g0a5mPEhV/nKsXRpYq9QtvruBSM2H9f/nN5ayP8WM0eztZGkAlAcEwSZp98grRMIdsxK9gao62HLcpaGcLCUA8L/niKayHxKj2Pomz3rvfvi+gP74sJk6bCwyPn98Uv27di76+7EBYaCjMzc3h92xwjRo2BVCrNx6iLllvH9+Dafj+4N22Lul0HAwAy0tNwec9GPLt+HpkZ6ShVsRrqdRsKQxPzXI355/aVCPzzGGp3GQhPr/aqDL/I8du8AWdPByA46DmkUn14Vq6C4aPGoEwZx88et/MXf/y2ZzfCw96/N5o0+xbDRnjzvUEqwTWUanLi+DEsWuiLQT8Nxe69B+Di4oohg/ohOjo62/63b/2NiePGoH2H/+HX3w6icZOmGDV8KJ48eZzPkRd+IbHJ6LfrjnybfPShfF95ayNMae6MO28SMPHwQ0w4FIjjgZGQZZ9LAgCkuloIjknGxssv8yH6ou3k8WNYvNAXg4YMxa69B1DexRU/DeqHmBzeF8eOHsaKpYsxaMgw7D90DNNnzcWpE8ewcvmSfI686IgIeoTA88dgUVIxWbn863qE3L2KZoMmoc24hXgXF41Ta+bkasygv/9CxPOHMDSzVEXIRd7fN66jU5cf4bd9N1av34yMjHQMG9wPyUlJOR5z4tgRrFq+BAMHD8XeA0cxdcYcBJw8jtUrluZj5IWLRIX/aQImlGqy3d8PHf7XGe3ad0Q5JydMmT4T+vr6OLh/X7b9d/yyDXXq1Ufvvv1Rtlw5DBsxCm4VKmD3zl/yOfLCL1MmIC45Q769Tc2U7+tTsySOPYjAgbvheBmXgjcJqbgUFIuMz2SUt14lYNffb3DtRVw+RF+0bd/2yfuinBOmTPvwvjiQ/fvizu1bqFylKlq1/h4lSpREnbr10KLVd7j3z918jrxoSE9JxplNP6NBz5GQGhrL21OT3uHhxVOo3XkASrhVhrWDMxr19kb4swcIfxb42THfxUbhr11r0aT/eGhpa6v6FIqklWs34vu27VHOyRnlXVwxY5YvwkJDERh4P8dj7ty+hUqVq6JFq+9gX6IEatWpi+YtWuP+vX/yMXLSJGpLKC9fvowjR44otG3btg2Ojo6wsbHBwIEDkZqaqqboVCs9LQ2BD+6jVu068jYtLS3UqlUHd+/cyvaYu7dvo1at2gptderWw93bt1UZapFU3ESKjV09sKaTO0Y2LAMrI10AgIm+DsrbGCM+OR1zW7tg8w+emNWyPFxtjdQcsWZIT3//vqhZS/F9UfMz74tKlavgwYP7+OdDAvnq5Utc/PM86tXnUpCvcXHnapT2/AYlK1RRaI968QSyzAyUcPu33bx4KRhb2CD8+cP/DiMnyGQ4s3kRKjX/HyxKOKgsbk2TmPgWAGBiYppjn0qVqyAw8L78j6tXr17ir4t/om79BvkSY2Ekkahu0wRqW0M5a9YsNGrUCN999x0A4J9//kG/fv3Qu3dvuLm54eeff4a9vT1mzJjx2XFSU1OzJJ6CtrRArxGJjYtFZmYmLC0Vp38sLS0RFPQ822OioqJgaWmVpX9UdJTK4iyKnkS+w6oLwXgTnwpzQ110qlwcc1q7YNT+B7At9v53pksVe/hff4Xg6CQ0dLLEjBblMfrAA4QmFM0/cAqK2Nic3xfBObwvWrX+HnGxsejT40cAAjIyMtCpc1f0Hzg4HyIuWp5eO4eokGdoP3l5ln1JCbHQ0tFRqFoCgIGJGZLiY3Ic8/aJvdDS1oJ707Z5Hq+mkslkWLzQF5UqV4WTc/kc+7Vo9R3iYmPRv3d3CBCQmZGBjp26oG//QfkYLWkStVUob9++jaZNm8p/3r17N2rWrImNGzfC29sbK1aswJ49e744jq+vL0xNTRW2nxf4qjJ0KsRuvUrA5eA4vIhNxu3XCZgb8BSGejqo62gOrQ9/RZ56FImzT6IRFJOMrdde4U18Cpo4c+1XQXT92lVs3rgek6ZMx649+7Fk2Spc+PM8Nqxbre7QCpXEmEhc2r0eTfqPh46uXp6MGfniCf45/Tsa9RkDiaaUaPLBgnmz8OzZE8xbuPiz/W5cvwa/zRswcfJU7Ni9Dz8vWYGLF/7EpvVr8inSwkcLEpVtmkBtFcrY2FjY2trKfz5//jxatmwp//mbb77By5dfvsjBx8cH3t7eCm2CdsGtTgKAuZk5tLW1s1yAEx0dDSsrq2yPsbKyQvR/qpHR0dGwssy+P+VOUlomQuNTYGcixT+h76eRXsWlKPR5FZcCK+O8+UeWcmZurvz7Ys2q5Wj9fRt0+F8nAIBzeRckJydh9sxp6D9wCLS0uEw8NyJfPEHy2zjsmz1M3ibIZAh9cg/3zx5Gq1FzIMvIQGpSokKVMjkhDoamFtmOGfrkHpLfxmHHhJ4KY17Zswn//HEQ3eb7q+6EiqgF82bj4p/nsWHLdtja2n2277rVK9DquzZo1+H9e8PJuTySk5Mxd/Z09B0wmO8NynNqSyhtbW0RFBSEUqVKIS0tDX///Tdmzpwp3//27Vvo6up+cRypNOv0dkpGnoebp3T19OBWoSKuXrmMJk29ALyfxrh69TK6/tA922M8K1fG1StX0L1nb3nblcuX4Fm5cj5EXHTp62jB1kSK2GfpiEhMQ/S7NNib6iv0KW6qj1uveBsgVdPVff++uHZV8X1x7TPvi5SUlCz/MH688EMQPnNpPiko4VYZnWasVWg757cEZsVLoXKLTjAyt4aWtg5eB95G2Wr1AABxYa+QGBMB27Ku2Y5ZvlZTlPxkzSUAHF02BeVrNYFL3W9VcyJFlCAIWOg7B+fO/IH1m/1RomTJLx6TkpKcpTLM98bnsZAujtoSylatWmHixIlYsGABDh48CENDQ9SvX1++/+7duyhXrpy6wlO5Hr36YOqkCahY0R3uHp74Zbs/kpOT0a59BwDAZJ/xsLGxxcjRYwAA3br3RL/ePeC/dQsaNGiIE8eP4f69e5g6Y5Y6T6PQ6flNCdx4GY/IxDRYGOqiSxV7yGQCLj6PBQD8/k84ulS1R3BMEoKjk9HI2RIlTPWx6Mwz+RjTWzjj2os4HA+MBPA+KbUz+fePGptiUpSxMEBiagai3qXn7wkWcj169sHUyRNQoaI73N09seOX9++Ltu3evy+mfHhfjPjwvmjQsDF+2eYHV9cK8PD0REhICNasXI4GDRtDm1cU55qeviEsSpRRaNOR6kNqVEze7lrvW1zesxFSo2LQMzDEX7vWwracG2zLucmP+XXqANRo3xuOVetC39gE+sYmCmNqaWvDwNQcZnZfTojoXwvmzcKJ40exeNkqGBoZISrq/WePsXEx6Ou//wN42uQJsLGxxbCR72fs6jdsjJ3bt8LF1Q3uHpXw8uULrFu9Ag0aNOJ7IwdMKMVRW0I5e/ZsdOjQAQ0bNoSxsTH8/f2hp/fvtOKWLVvw7bdF96/YFi1bITYmBmtWrUBUVCRcXN2wZv0mWH6Y2gsLDYWW5N/KS+UqVeG7cBFWrViGlcuWoLRDGSxbuRrOn1mUTVlZGulhdCNHFJPqICElA4HhifA58hAJH8raRx9EQE9Hgj41SsFYqo3gmGTMOvkY4W/T5GPYFZOimP6/b51yVoaY1cpF/nOfmqUAAGefRGHVhRf5dGZFQ/OWrRAbG4O1n74v1v37vggNDYXkk4rkgEFDIJFIsHrlMkREhMPc3AINGjXGsBGj1XUKRVbtLoMAiRYC1s5BZkY6Slashvrdhir0iQt7hbTknO+NSF/ntz27AQCD+vVSaJ8+ax6+b/v+JvFhYaEK1fp+AwZDIpFg7eoViIwIh5m5BRo0bISfho3Kt7hJs0gENde+4+PjYWxsnOUvppiYGBgbGyskmblV0Ke8NUm3bTfVHQJ94pce1dQdAn2w9nL2V65T/htQo4y6Q6APiumrb21nQKDq7prSzK3oX++g9q9eNDXN/j5aFhbZL/QmIiIiooJF7QklERERkbppcQ2lKLxvABERERGJwgolERERaTyJhtyAXFVYoSQiIiIiUVihJCIiIo3H+1CKw4SSiIiINB6nvMXhlDcRERERicIKJREREWk83jZIHFYoiYiIiEgUViiJiIhI43ENpTisUBIRERGRKKxQEhERkcbjbYPEYYWSiIiIiERhhZKIiIg0HguU4jChJCIiIo2nxTlvUTjlTURERESisEJJREREGo/1SXFYoSQiIiIiUVihJCIiImKJUhRWKImIiIhIFFYoiYiISOPxqxfFYYWSiIiIiERhhZKIiIg0Hm9DKQ4TSiIiItJ4zCfF4ZQ3EREREYnCCiURERERS5SisEJJRERERKKwQklEREQaj7cNEocVSiIiIiIShRVKIiIi0ni8bZA4rFASERERkSisUBIREZHGY4FSHCaURERERMwoReGUNxEREVEB4evri2+++QbFihWDjY0N2rVrh0ePHin0SUlJwdChQ2FpaQljY2N07NgR4eHhaor4PSaUREREpPEkKvxPGefPn8fQoUNx5coVBAQEID09Hd9++y3evXsn7zN69GgcPnwYe/fuxfnz5/HmzRt06NAhr58SpXDKm4iIiKiAOHHihMLPW7duhY2NDW7evIkGDRogPj4emzdvxs6dO9GkSRMAgJ+fH9zc3HDlyhXUqlVLHWEzoSQiIiJS5W2DUlNTkZqaqtAmlUohlUq/eGx8fDwAwMLCAgBw8+ZNpKenw8vLS97H1dUVpUuXxuXLl9WWUHLKm4iIiEiFfH19YWpqqrD5+vp+8TiZTIZRo0ahbt26cHd3BwCEhYVBT08PZmZmCn1tbW0RFhamivBzhRVKIiIi0niqvMjbx8cH3t7eCm25qU4OHToU9+7dw8WLF1UVWp5hQkkqVamUqbpDoE/wmyAKjp/qlFV3CPRBt2031R0CfbCvbzV1h6ASuZ3e/tSwYcNw5MgR/PnnnyhZsqS83c7ODmlpaYiLi1OoUoaHh8POzi6vQlYap7yJiIiIJCrclCAIAoYNG4YDBw7gzJkzcHR0VNhfrVo16Orq4vTp0/K2R48eISQkBLVr11buwfIQK5RERESk8ZS9vY+qDB06FDt37sTvv/+OYsWKyddFmpqawsDAAKampujXrx+8vb1hYWEBExMTDB8+HLVr11bbBTkAE0oiIiKiAmPt2rUAgEaNGim0+/n5oXfv3gCApUuXQktLCx07dkRqaiqaN2+ONWvW5HOkiphQEhERkcYrKGvMBUH4Yh99fX2sXr0aq1evzoeIcodrKImIiIhIFFYoiYiISOMVkAJlocUKJRERERGJwgolEREREUuUorBCSURERESisEJJREREGq+g3IeysGKFkoiIiIhEYYWSiIiINF5BuQ9lYcWEkoiIiDQe80lxOOVNRERERKKwQklERETEEqUorFASERERkSisUBIREZHG422DxGGFkoiIiIhEYYWSiIiINB5vGyQOK5REREREJAorlERERKTxWKAUhwklERERETNKUTjlTURERESisEJJREREGo+3DRKHFUoiIiIiEoUVSiIiItJ4vG2QOKxQEhEREZEorFASERGRxmOBUhxWKImIiIhIFFYoiYiIiFiiFIUJJREREWk83jZIHE55ExEREZEorFASERGRxuNtg8RhhZKIiIiIRGGFkoiIiDQeC5TisEJJRERERKKwQklERETEEqUorFASERERkSisUBIREZHG430oxWFCSURERBqPtw0ShwmlGu3euQP+fpsRFRWJ8i6umDhpKjw8PbPtu2/vHhw+dBBPnz4BAFSoUBHDR3rn2J++7P6pPbh9yB8ujdqi+v8GAgCu7lqJsEe3kRwfAx2pPqwd3VC5bR+Y2pXK1ZhXd63C07+Oo1rHAXBt3E6F0Rddyrwvnj59gjUrVyDwwX28efMa4yb4oHvP3vkbcBHGz6j817lKcXSpYq/Q9jouBSP235f/XN7aCD9Ws4eztRFkAhAck4TZJ58gLVPIdsz2nnao5WCGEmb6SMuQ4VHEO2y//gpvElJVei6kWbiGUk1OHD+GRQt9Meinodi99wBcXFwxZFA/REdHZ9v/xvWraNmqNTZt2YbtO3bD1q44hgzsi/Dw8HyOvGiIfvEYT/46AbMSjgrtFqWcUKv7aHw3ZR2aDJ0NQRBwZvVUyGSZXxzz5Z1LiA5+CANTS1WFXeQp+75ISU5GyVIlMWL0GFhZWedztEUbP6PUJyQ2Gf123ZFvk48+lO8rb22EKc2dcedNAiYefogJhwJxPDASsuxzSQBARTtjnAiMhM/hh5h58gm0tSSY1sIZUh2mAJ+SqHDTBGr7bbp37566HrpA2O7vhw7/64x27TuinJMTpkyfCX19fRzcvy/b/r4LF6PLD93g6uYGx7LlMGPWHMhkMly7cjmfIy/80lOT8dfWn1Hzh+HQMzBW2OdcryVsndxhbGkLi1JOqPR9TyTFRuJddMRnx0yKi8L1vetQp/c4aGlrqzL8Ik3Z94W7hye8x05Ay1atoaenl8/RFm38jFKfTJmAuOQM+fY29d8/aPvULIljDyJw4G44Xsal4E1CKi4FxSLjMxnlnFNPcfZpNF7GpeBFTDJWXQiGtbEU5SwN8+N0SEOoLaH09PREzZo1sXHjRrx9+1ZdYahFeloaAh/cR63adeRtWlpaqFWrDu7euZWrMVJSkpGRkQETU1NVhVlkXf91LUq4f4PirlU+2y8jNQXPrwTA2NIWhuZWOfYTZDJc2rYYFZp2hFlxh7wOV2PkxfuC8gY/o9SruIkUG7t6YE0nd4xsWAZWRroAABN9HZS3MUZ8cjrmtnbB5h88MatlebjaGik1vqHu+z9636Zm5HnshZlEorpNE6gtoTx//jwqVqyIMWPGoHjx4ujVqxcuXLigrnDyVWxcLDIzM2FpqTg1amlpiaioqFyNsWzxIljb2Ch84NOXBd84j5iXT1G5Te8c+zz+8wh+9e6IX8d0xJsHN9Fk2Fxo6+jm2P9+wG+QaGnDpVEbFUSsOfLifUF5g59R6vMk8h1WXQjGnJNPseFSCGyMpZjT2gX6OlqwLSYFAHSpYo8/HkdhzskneB6dhBktyqO4iTRX40vwvsoZGJ6Il3EpKjwT0jRqSyjr16+PLVu2IDQ0FCtXrkRwcDAaNmyI8uXLY8GCBQgLC8vVOKmpqUhISFDYUlOL9kLjzRs34MTxY1i6fBWk0tx9iBDwLjYSN/dtQN3e46Ctm/P0aJlvGqPlxBXwGrUAxaztcXGLLzLT07LtGx3yBI/O/Y7a3UdDoil/hhJ9AT+jvt6tVwm4HByHF7HJuP06AXMDnsJQTwd1Hc2h9eEj5tSjSJx9Eo2gmGRsvfYKb+JT0MQ5d2u3B9QujdLmBlhy9rkKz6Kw4ipKMdS+ItfIyAh9+vTB+fPn8fjxY3Tq1AmrV69G6dKl0abNlys+vr6+MDU1Vdh+XuCbD5F/PXMzc2hra2dZ3B4dHQ0rq5ynVgHA328z/DZvwLqNm1HexVWVYRY5MSFPkfI2DscXjMDOEd9j54jvEfH0Hzw6fwg7R3wvv/BGz8AIJjYlYOvkjvr9JyE+/BVe3rmU7ZiRz+4jJTEeB6f1lo/5LiYCf+/fjIPT+uTn6RV6Yt4XlLf4GVVwJKVlIjQ+BXYmUsQmpQMAXv2nsvgqLgVWxl9eQ9y/VilUK2WK6ccfI+bDWER5pUDdNsjJyQmTJk2Cg4MDfHx8cPTo0S8e4+PjA29vb4U2Qbtg/0Wsq6cHtwoVcfXKZTRp6gUAkMlkuHr1Mrr+0D3H4/w2b8SmDeuwdsNmVHT3yK9wiww7l0poPWm1QtvlX5bBxLYkKjb7H7S0srmYRni/ZWZk/+Hr+E0T2LlUVmg7s3oaHGs0RrlazfIocs3wte8Lynv8jCo49HW0YGsiReyzdEQkpiH6XRrsTfUV+hQ31cetV/GfHad/rVKo4WCG6ccfIyIx+xkXTcdJJnEKTEL5559/YsuWLdi3bx+0tLTQuXNn9OvX74vHSaXSLFMqKYVgnXGPXn0wddIEVKzoDncPT/yy3R/Jyclo174DAGCyz3jY2Nhi5OgxAIAtmzZgzaoVmL9wMeztSyAqMhIAYGhoCEMj5RZkaypdfUOY2ZdRaNPR04fUyARm9mXwNioUL25eQHG3KtA3NkVSXBTun9oLbV09lKj4jfyYw7MHoXKbXihVqQ6kxiaQGpsojKmlrQ0DE3OY2JbMj9MqUpR9X6SnpeHZs2fv/z89DRER4XgYGAhDQ0OUduAFUmLwM0o9en5TAjdexiMyMQ0WhrroUsUeMpmAi89jAQC//xOOLlXtERyThODoZDRytkQJU30sOvNMPsb0Fs649iIOxwPfvwYDapdC/bIWmH/6GZLTM2Fm8P6f/qS0zBzvXamJmE+Ko9aE8s2bN9i6dSu2bt2Kp0+fok6dOlixYgU6d+4MoyL+AdSiZSvExsRgzaoViIqKhIurG9as3wTLD9NJYaGh0JL8uyJh76+7kZ6ejjGjRyiMM/inYRgydHi+xl5UaevoIfLZfTw69zvSkhKhX8wMNk7uaD5mEfSLmcn7JYS/QlryO/UFWoQp+76IiIxAl/+1k//s77cF/n5bUP2bGti8dXt+h1+k8DNKPSyN9DC6kSOKSXWQkJKBwPBE+Bx5iIQPlZKjDyKgpyNBnxqlYCzVRnBMMmadfIzwt/9WHe2KSVFM/99/3lu42QAAZrdyUXisVX8G4+zT7O8rSqQsiSAIavnzpGXLlvjjjz9gZWWFnj17om/fvnBxcfnygblQGCqUmmLh2afqDoE+Mb6xk7pDICpwum27qe4Q6IN9faup7bFD41W3FKC4adG/T67aKpS6urr47bff8N1330GbN4ImIiIiKrTUllAeOnRIXQ9NREREpEDCVZSiqP22QURERERUuBWYq7yJiIiI1IYFSlFYoSQiIiIiUVihJCIiIo3HAqU4TCiJiIhI4/GbcsThlDcRERERicIKJREREWk83jZIHFYoiYiIiEgUViiJiIiIWKAUhRVKIiIiIhKFFUoiIiLSeCxQisMKJRERERGJwgolERERaTzeh1IcJpRERESk8XjbIHE45U1EREREorBCSURERBqPU97isEJJRERERKIwoSQiIiIiUZhQEhEREZEoXENJREREGo9rKMVhhZKIiIiIRGGFkoiIiDQe70MpDhNKIiIi0nic8haHU95EREREJAorlERERKTxWKAUhxVKIiIiIhKFFUoiIiIilihFYYWSiIiIiERhhZKIiIg0Hm8bJA4rlEREREQkCiuUREREpPF4H0pxWKEkIiIiIlFYoSQiIiKNxwKlOEwoiYiIiJhRisIpbyIiIiIShQklERERaTyJCv/7GqtXr0aZMmWgr6+PmjVr4tq1a3l8xnmLCSURERFRAfLrr7/C29sb06dPx99//41KlSqhefPmiIiIUHdoOWJCSURERBpPIlHdpqwlS5ZgwIAB6NOnDypUqIB169bB0NAQW7ZsyfsTzyNMKImIiIhUKDU1FQkJCQpbampqtn3T0tJw8+ZNeHl5ydu0tLTg5eWFy5cv51fISiuSV3nrF4GzSk1Nha+vL3x8fCCVStUdzleb1sxJ3SGIVlRei6KAr0XBUZRei319q6k7BNGK0uuhLqrMHWbM8cXMmTMV2qZPn44ZM2Zk6RsVFYXMzEzY2toqtNva2uLhw4eqC1IkiSAIgrqDoKwSEhJgamqK+Ph4mJiYqDscjcbXouDga1Fw8LUoWPh6FGypqalZKpJSqTTb5P/NmzcoUaIELl26hNq1a8vbx48fj/Pnz+Pq1asqj/drFIFaHhEREVHBlVPymB0rKytoa2sjPDxcoT08PBx2dnaqCC9PcA0lERERUQGhp6eHatWq4fTp0/I2mUyG06dPK1QsCxpWKImIiIgKEG9vb/Tq1QvVq1dHjRo1sGzZMrx79w59+vRRd2g5YkJZQEmlUkyfPp2LqwsAvhYFB1+LgoOvRcHC16No6dKlCyIjIzFt2jSEhYWhcuXKOHHiRJYLdQoSXpRDRERERKJwDSURERERicKEkoiIiIhEYUJJRERERKIwoSQiIiIiUZhQFjC9e/eGRCLJsj19+lTdoWmUj6/D/PnzFdoPHjwIiUSipqiICobIyEgMGTIEpUuXhlQqhZ2dHZo3b46//vpL3aFpjO+//x4tWrTIdt+FCxcgkUhw9+7dfI6KNBkTygKoRYsWCA0NVdgcHR3VHZbG0dfXx4IFCxAbG6vuUAjAy5cv0bdvX9jb20NPTw8ODg4YOXIkoqOj1R2axunYsSNu3boFf39/PH78GIcOHUKjRo34WuSjfv36ISAgAK9evcqyz8/PD9WrV4enp6caIiNNxYSyAPr4F/+nm7a2trrD0jheXl6ws7ODr6+vukPReM+fP0f16tXx5MkT7Nq1C0+fPsW6devk3xwRExOj7hA1RlxcHC5cuIAFCxagcePGcHBwQI0aNeDj44M2bdqoOzyN8d1338Ha2hpbt25VaE9MTMTevXvRr18/9QRGGosJJVEOtLW1MW/ePKxcuTLbKgDln6FDh0JPTw+nTp1Cw4YNUbp0abRs2RJ//PEHXr9+jcmTJ6s7RI1hbGwMY2NjHDx4EKmpqeoOR2Pp6OigZ8+e2Lp1Kz69nfTevXuRmZmJH374QY3RkSZiQlkAHTlyRP6hbWxsjE6dOqk7JI3Vvn17VK5cGdOnT1d3KBorJiYGJ0+exE8//QQDAwOFfXZ2dujWrRt+/fVX8Dsa8oeOjg62bt0Kf39/mJmZoW7dupg0aRLX66lB37598ezZM5w/f17e5ufnh44dO8LU1FSNkZEmYkJZADVu3Bi3b9+WbytWrFB3SBptwYIF8Pf3R2BgoLpD0UhPnjyBIAhwc3PLdr+bmxtiY2MRGRmZz5Fpro4dO+LNmzc4dOgQWrRogXPnzqFq1apZpl9JtVxdXVGnTh1s2bIFAPD06VNcuHCB092kFkwoCyAjIyM4OTnJt+LFi6s7JI3WoEEDNG/eHD4+PuoORaN9qQKpp6eXT5EQ8P6itWbNmmHq1Km4dOkSevfuzUq+GvTr1w/79u3D27dv4efnh3LlyqFhw4bqDos0EBNKolyYP38+Dh8+jMuXL6s7FI3j5OQEiUSSY4U4MDAQ1tbWMDMzy9/ASEGFChXw7t07dYehcTp37gwtLS3s3LkT27ZtQ9++fXlrM1ILJpREueDh4YFu3bpx+YEaWFpaolmzZlizZg2Sk5MV9oWFhWHHjh3o3bu3eoLTQNHR0WjSpAl++eUX3L17F0FBQdi7dy8WLlyItm3bqjs8jWNsbIwuXbrAx8cHoaGhfC+Q2jChJMqlWbNmQSaTqTsMjbRq1SqkpqaiefPm+PPPP/Hy5UucOHECzZo1Q/ny5TFt2jR1h6gxjI2NUbNmTSxduhQNGjSAu7s7pk6digEDBmDVqlXqDk8j9evXD7GxsWjevDns7e3VHQ5pKInASyOJqBAIDg7GjBkzcOLECUREREAQBHTo0AHbt2+HoaGhusMjItJoTCiJqFCaPn06lixZgoCAANSqVUvd4RARaTQmlERUaPn5+SE+Ph4jRoyAlhZX8BARqQsTSiIiIiIShX/SExEREZEoTCiJiIiISBQmlEREREQkChNKIiIiIhKFCSURERERicKEkojyTO/evdGuXTv5z40aNcKoUaPyPY5z585BIpEgLi5OZY/x33P9GvkRJxFRfmBCSVTE9e7dGxKJBBKJBHp6enBycsKsWbOQkZGh8sfev38/Zs+enau++Z1clSlTBsuWLcuXxyIiKup01B0AEaleixYt4Ofnh9TUVBw7dgxDhw6Frq4ufHx8svRNS0uDnp5enjyuhYVFnoxDREQFGyuURBpAKpXCzs4ODg4OGDJkCLy8vHDo0CEA/07dzp07F/b29nBxcQEAvHz5Ep07d4aZmRksLCzQtm1bBAcHy8fMzMyEt7c3zMzMYGlpifHjx+O/35Pw3ynv1NRUTJgwAaVKlYJUKoWTkxM2b96M4OBgNG7cGABgbm4OiUSC3r17AwBkMhl8fX3h6OgIAwMDVKpUCb/99pvC4xw7dgzly5eHgYEBGjdurBDn18jMzES/fv3kj+ni4oLly5dn23fmzJmwtraGiYkJBg8ejLS0NPm+3MRORFQUsEJJpIEMDAwQHR0t//n06dMwMTFBQEAAACA9PR3NmzdH7dq1ceHCBejo6GDOnDlo0aIF7t69Cz09PSxevBhbt27Fli1b4ObmhsWLF+PAgQNo0qRJjo/bs2dPXL58GStWrEClSpUQFBSEqKgolCpVCvv27UPHjh3x6NEjmJiYwMDAAADg6+uLX375BevWrYOzszP+/PNPdO/eHdbW1mjYsCFevnyJDh06YOjQoRg4cCBu3LiBMWPGiHp+ZDIZSpYsib1798LS0hKXLl3CwIEDUbx4cXTu3FnhedPX18e5c+cQHByMPn36wNLSEnPnzs1V7ERERYZAREVar169hLZt2wqCIAgymUwICAgQpFKpMHbsWPl+W1tbITU1VX7M9u3bBRcXF0Emk8nbUlNTBQMDA+HkyZOCIAhC8eLFhYULF8r3p6enCyVLlpQ/liAIQsOGDYWRI0cKgiAIjx49EgAIAQEB2cZ59uxZAYAQGxsrb0tJSREMDQ2FS5cuKfTt16+f8MMPPwiCIAg+Pj5ChQoVFPZPmDAhy1j/5eDgICxdujTH/f81dOhQoWPHjvKfe/XqJVhYWAjv3r2Tt61du1YwNjYWMjMzcxV7dudMRFQYsUJJpAGOHDkCY2NjpKenQyaT4ccff8SMGTPk+z08PBTWTd65cwdPnz5FsWLFFMZJSUnBs2fPEB8fj9DQUNSsWVO+T0dHB9WrV88y7f3R7du3oa2trVRl7unTp0hKSkKzZs0U2tPS0lClShUAQGBgoEIcAFC7du1cP0ZOVq9ejS1btiAkJATJyclIS0tD5cqVFfpUqlQJhoaGCo+bmJiIly9fIjEx8YuxExEVFUwoiTRA48aNsXbtWujp6cHe3h46OopvfSMjI4WfExMTUa1aNezYsSPLWNbW1l8Vw8cpbGUkJiYCAI4ePYoSJUoo7JNKpV8VR27s3r0bY8eOxeLFi1G7dm0UK1YMP//8M65evZrrMdQVOxGROjChJNIARkZGcHJyynX/qlWr4tdff4WNjQ1MTEyy7VO8eHFcvXoVDRo0AABkZGTg5s2bqFq1arb9PTw8IJPJcP78eXh5eWXZ/7FCmpmZKW+rUKECpFIpQkJCcqxsurm5yS8w+ujKlStfPsnP+Ouvv1CnTh389NNP8rZnz55l6Xfnzh0kJyfLk+UrV67A2NgYpUqVgoWFxRdjJyIqKniVNxFl0a1bN1hZWaFt27a4cOECgoKCcO7cOYwYMQKvXr0CAIwcORLz58/HwYMH8fDhQ/z000+fvYdkmTJl0KtXL/Tt2xcHDx6Uj7lnzx4AgIODAyQSCY4cOYLIyEgkJiaiWLFiGDt2LEaPHg1/f388e/YMf//9N1auXAl/f38AwODBg/HkyROMGzcOjx49ws6dO7F169Zcnefr169x+/ZthS02NhbOzs64ceMGTp48icePH2Pq1Km4fv16luPT0tLQr18/PHjwAMeOHcP06dMxbNgwaGlp5Sp2IqIiQ92LOIlItT69KEeZ/aGhoULPnj0FKysrQSqVCmXLlhUGDBggxMfHC4Lw/iKckSNHCiYmJoKZmZng7e0t9OzZM8eLcgRBEJKTk4XRo0cLxYsXF/T09AQnJydhy5Yt8v2zZs0S7OzsBIlEIvTq1UsQhPcXEi1btkxwcXERdHV1BWtra6F58+bC+fPn5ccdPnxYcHJyEqRSqVC/fn1hy5YtubooB0CWbfv27UJKSorQu3dvwdTUVDAzMxOGDBkiTJw4UahUqVKW523atGmCpaWlYGxsLAwYMEBISUmR9/lS7Lwoh4iKCokg5LCCnoiIiIgoFzjlTURERESiMKEkIiIiIlGYUBIRERGRKEwoiYiIiEgUJpREREREJAoTSiIiIiIShQklEREREYnChJKIiIiIRGFCSURERESiMKEkIiIiIlGYUBIRERGRKP8H4D1VKaummsAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
        "from sklearn.preprocessing import label_binarize\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from itertools import cycle\n",
        "from scipy import interp\n",
        "\n",
        "# Assuming you have trained and compiled your CNN model\n",
        "# model = ...\n",
        "\n",
        "# Get predictions for the validation set\n",
        "y_val_pred_prob = model.predict(X_val)\n",
        "y_val_pred = np.argmax(y_val_pred_prob, axis=1)\n",
        "\n",
        "# Your true class labels (replace with your actual variable)\n",
        "true_classes = y_val\n",
        "\n",
        "# Your class labels (replace with your actual class labels)\n",
        "class_labels = ['N', 'S', 'V', 'F', 'Q']  # Replace with your arrhythmia class labels\n",
        "\n",
        "# Binarize the true classes for ROC curve calculation\n",
        "y_test_binarized = label_binarize(true_classes, classes=np.arange(len(class_labels)))\n",
        "\n",
        "# Compute ROC curve and ROC area for each class\n",
        "fpr = dict()\n",
        "tpr = dict()\n",
        "roc_auc = dict()\n",
        "n_classes = len(class_labels)\n",
        "\n",
        "for i in range(n_classes):\n",
        "    fpr[i], tpr[i], _ = roc_curve(y_test_binarized[:, i], y_val_pred_prob[:, i])\n",
        "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
        "\n",
        "# Compute macro-average ROC curve and ROC area\n",
        "all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
        "mean_tpr = np.zeros_like(all_fpr)\n",
        "for i in range(n_classes):\n",
        "    mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
        "\n",
        "mean_tpr /= n_classes\n",
        "fpr[\"macro\"] = all_fpr\n",
        "tpr[\"macro\"] = mean_tpr\n",
        "roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n",
        "\n",
        "# Plot all ROC curves with a border around the entire plot\n",
        "plt.figure(figsize=(8, 6))\n",
        "ax = plt.gca()\n",
        "\n",
        "colors = cycle(['blue', 'red', 'green', 'orange', 'purple'])\n",
        "for i, color in zip(range(n_classes), colors):\n",
        "    plt.plot(fpr[i], tpr[i], color=color, lw=2,\n",
        "             label='ROC curve of {0} (area = {1:0.2f})'.format(class_labels[i], roc_auc[i]))\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--', lw=2)\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('CNN-LSTM: Multi-Class ROC Analysis')\n",
        "\n",
        "# Add a border around the entire plot\n",
        "for spine in ax.spines.values():\n",
        "    spine.set_edgecolor('black')\n",
        "    spine.set_linewidth(2)\n",
        "\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "LAIXCs9CNRHY",
        "outputId": "64d43727-d66f-4205-e4d0-bd890ec21b48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "522/522 [==============================] - 6s 11ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-53-d741f14902c6>:38: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n",
            "  mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot training & validation accuracy values with a border around the entire plot\n",
        "plt.figure(figsize=(12, 5))\n",
        "ax1 = plt.subplot(1, 2, 1)\n",
        "ax1.plot(history.history['accuracy'], color='blue', label='Train')\n",
        "ax1.plot(history.history['val_accuracy'], color='red', label='Validation')\n",
        "ax1.set_title('Model Accuracy of CNN-LSTM')\n",
        "ax1.set_ylabel('Accuracy')\n",
        "ax1.set_xlabel('Epoch')\n",
        "ax1.legend(loc='upper left')\n",
        "\n",
        "# Plot training & validation loss values with a border around the entire plot\n",
        "ax2 = plt.subplot(1, 2, 2)\n",
        "ax2.plot(history.history['loss'], color='green', label='Train')\n",
        "ax2.plot(history.history['val_loss'], color='orange', label='Validation')\n",
        "ax2.set_title('Model Loss of CNN-LSTM')\n",
        "ax2.set_ylabel('Loss')\n",
        "ax2.set_xlabel('Epoch')\n",
        "ax2.legend(loc='upper left')\n",
        "\n",
        "# Add a border around the entire figure\n",
        "for ax in [ax1, ax2]:\n",
        "    for spine in ax.spines.values():\n",
        "        spine.set_edgecolor('black')\n",
        "        spine.set_linewidth(2)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "yPJeSs6zPVeS",
        "outputId": "0cd2407f-b652-46f9-b645-46d8df5ed329"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}